Data Camp Python Notes

Background and Overview

DataCamp offer interactive courses related to Python Programming. Since R Markdown documents can run simple Python code chunks (though the data is not accessible to future chunks, a large difference from R Markdown for R), this document attempts to summarize notes from the first module.

Python Programming

Intro to Python for Data Science

Chapter 1 - Python Basics

Hello Python! - focusing on Python specific to data science:

• Designed by Guido Van Rossum (started as a hobby), but has become a general purpose language that can build anything
  
• Python is open-source, free, and has packages for data science
  
• This course will focus on Python 3.x given that support for Python 2.7 has been (and will continue to) decreasing
  • Available at https://www.python.org/downloads (have downloaded Python 3.6.1 for Windows)
    
  • The DataCamp module uses interactive Python (iPython)
    
• Python scripts are simply text files with a .py extension - must use print() inside scripts in order to force printing

Variables and Types - variables names are case-sensitive in Python:

• The single equals sign is the assignment operator
  
• The type(myVar) call will return the type of the variable - float, integer (“int”), string (“str”), boolean (“bool”), etc.
  • The booleans are represented as proper-noun capitalization - True and False
    
• String summation is concatenation without spacing (roughly the same as paste0() in R) – “ab” + “cd” = “abcd” ; note also that “ab” * 2 = “abab”
  • In general, different types of data will respond differently to the same function

Example code includes:

# Example, do not modify!
print(5 / 8)

# Put code below here
print(7 + 10)

# Recall that commented lines are marked by the hash-sign, same as R
# Exponentiation is ** and modulo division is %

# Addition and subtraction
print(5 + 5)
print(5 - 5)

# Multiplication and division
print(3 * 5)
print(10 / 2)

# Exponentiation
print(4 ** 2)

# Modulo
print(18 % 7)

# How much is your $100 worth after 7 years?
print(100 * 1.1**7)


# Create a variable savings
savings = 100

# Print out savings
print(savings)


# Create a variable savings
savings = 100

# Create a variable factor
factor = 1.10

# Calculate result
result = savings * factor ** 7

# Print out result
print(result)


# Create a variable desc
desc = "compound interest"

# Create a variable profitable
profitable = True


# Several variables to experiment with
savings = 100
factor = 1.1
desc = "compound interest"

# Assign product of factor and savings to year1
year1 = savings * factor

# Print the type of year1
print(type(year1))

# Assign sum of desc and desc to doubledesc
doubledesc = desc + desc

# Print out doubledesc
print(doubledesc)


# Definition of savings and result
savings = 100
result = 100 * 1.10 ** 7

# Fix the printout
print("I started with $" + str(savings) + " and now have $" + str(result) + ". Awesome!")

# Definition of pi_string
pi_string = "3.1415926"

# Convert pi_string into float: pi_float
pi_float = float(pi_string)

## 0.625
## 17
## 10
## 0
## 15
## 5.0
## 16
## 4
## 194.87171000000012
## 100
## 194.87171000000012
## <class 'float'>
## compound interestcompound interest
## I started with $100 and now have $194.87171000000012. Awesome!

The output all comes at once, another difference from R Markdown for R. In combination with being unable to access any of the variables later in the same document, there are tangible limitations to this approach.

Using Python within R Markdown may be more useful if I install “feather” for both Python and R. Feather allows for running code in Python, then quick-saving pandas in a way that is quick-readable as frames for the next R chunk. See https://blog.rstudio.org/2016/03/29/feather/.

Getting feather for R took just a few seconds using install.packages(). Getting feather for Python 3.6 using Windows seems to require a C++ 14.0 compiler from MS Visual Studio. So far, that is easier said than done.

---

Chapter 2 - Lists

What are lists? Multiple vales in one variable, formed using square brackets such as myList = [a, b, c]:

• The elements of a list may be of any type, including lists

Subsetting lists - the first element in the list is defined as element 0:

• Subsetting can be done as myList[myIndex]
  
• Alternately, subsetting can be done using negative numbers, with -1 being the last element of the list
  
• List slicing can be run using the colon operator
  • myList[a:b] will start with index a and end with index b-1
    
  • myList[:b] means go from start to index b-1, while myList[a:] means go from a to the end of the list

List manipulation - changing, adding, or removing elements:

• Changing elements is based on using the indices and the equal sign - myList[myIndex] = myNewValue
  
• The addition operator will concatenate the various lists
  • myList + [a, b] will produce a new list [myList, a, b]
    
• Deleting elements from a list uses the del() operator - for example, del(myList[2]) will delete the third item of myList which occupies index 2
  
• Behind the scense, Python is storing the data and the references to the data
  • Importantly, this means that copying a list and then editing the copy will edit the original list also; the pointers are to the same underlying data
    
  • Basically, myNewList = myList is copying the references to the data that are contained in myList, rather than copying all the data and the references
    
  • On the other hand, myNewList = myList[:] or myNewList = list(myList) will make the full, independent copy of the data with new references

Example code includes:

# area variables (in square meters)
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50

# Create list areas
areas = [hall, kit, liv, bed, bath]

# Print areas
print(areas)


# area variables (in square meters)
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50

# Adapt list areas
areas = ["hallway", hall, "kitchen", kit, "living room", liv, "bedroom", bed, "bathroom", bath]

# Print areas
print(areas)


# area variables (in square meters)
hall = 11.25
kit = 18.0
liv = 20.0
bed = 10.75
bath = 9.50

# house information as list of lists
house = [["hallway", hall],
         ["kitchen", kit],
         ["living room", liv],
         ["bedroom", bed], 
         ["bathroom", bath]
         ]

# Print out house
print(house)

# Print out the type of house
print(type(house))


# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Print out second element from areas
print(areas[1])

# Print out last element from areas
print(areas[-1])

# Print out the area of the living room
print(areas[5])


# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Sum of kitchen and bedroom area: eat_sleep_area
eat_sleep_area = areas[3] + areas[7]

# Print the variable eat_sleep_area
print(eat_sleep_area)


# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Use slicing to create downstairs
downstairs = areas[:6]

# Use slicing to create upstairs
upstairs = areas[6:]

# Print out downstairs and upstairs
print(downstairs)
print(upstairs)


# Create the areas list
areas = ["hallway", 11.25, "kitchen", 18.0, "living room", 20.0, "bedroom", 10.75, "bathroom", 9.50]

# Correct the bathroom area
areas[-1] = 10.5

# Change "living room" to "chill zone"
areas[4] = "chill zone"


# Create the areas list and make some changes
areas = ["hallway", 11.25, "kitchen", 18.0, "chill zone", 20.0,
         "bedroom", 10.75, "bathroom", 10.50]

# Add poolhouse data to areas, new list is areas_1
areas_1 = areas + ["poolhouse", 24.5]

# Add garage data to areas_1, new list is areas_2
areas_2 = areas_1 + ["garage", 15.45]


# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Create areas_copy
areas_copy = list(areas)

# Change areas_copy
areas_copy[0] = 5.0

# Print areas
print(areas)

## [11.25, 18.0, 20.0, 10.75, 9.5]
## ['hallway', 11.25, 'kitchen', 18.0, 'living room', 20.0, 'bedroom', 10.75, 'bathroom', 9.5]
## [['hallway', 11.25], ['kitchen', 18.0], ['living room', 20.0], ['bedroom', 10.75], ['bathroom', 9.5]]
## <class 'list'>
## 11.25
## 9.5
## 20.0
## 28.75
## ['hallway', 11.25, 'kitchen', 18.0, 'living room', 20.0]
## ['bedroom', 10.75, 'bathroom', 9.5]
## [11.25, 18.0, 20.0, 10.75, 9.5]

---

Chapter 3 - Functions and Packages

Introduction to functions - pieces of reusable code for solving a particular task:

• Built-in functions are things like max() or type() or round(myNum, myDecimals)
  
• Can use help(builtInFunction) to get the help page for builtInFunction

Methods - all objects of a specific type have default access to the methods for that object:

• Methods are functions that belong to an object
  
• For example, myList.index(“mySearch”) will return the index that matches to “mySearch” (if a number, should not be quoted)
  • Alternately, myList.count(“mySearch”) will return the number of matches to “mySearch”
    
• The methods will behave differently (perhaps even not existing) for different object types
  
• Further, some methods modify the object that they are associated with; for example .append()

Packages are directoried of pyhton scripts, each a module specifying functions, methods, and types:

• Thousands of Python packages are available, including Numpy, Matplotlib, and Scikit-learn
  
• Installing packages is based on the “pip” system - download get-pip.py from http://pip.readthedocs.org/en/stable/installing
  • Then, uses “pip3 install myPackage” (unquoted) at the command line
    
  • On my machine, needs to be at command line, then [PythonPath].exe -m pip install myPackage
    
• Packages can then be imported using “import myPackage” (unquoted) at the command line
  
• The package always needs to be attached to its command, for example numpy.array() rather than just array()
  • As a result, it is often helpful to use import numpy as np, so that np.array() can serve as a shortcut for numpy.array()
    
• Alternately, can ask for “from numpy import array” if only wanting to import the function array()
  • Now, array() can also be called without any prefix; for example, as array(myNumbers) rather than numpy.array(myNumbers)

Example code includes:

# Create variables var1 and var2
var1 = [1, 2, 3, 4]
var2 = True

# Print out type of var1
print(type(var1))

# Print out length of var1
print(len(var1))

# Convert var2 to an integer: out2
out2 = int(var2)


# Create lists first and second
first = [11.25, 18.0, 20.0]
second = [10.75, 9.50]

# Paste together first and second: full
full = first + second

# Sort full in descending order: full_sorted
full_sorted = sorted(full, reverse=True)

# Print out full_sorted
print(full_sorted)


# string to experiment with: room
room = "poolhouse"

# Use upper() on room: room_up
room_up = room.upper()

# Print out room and room_up
print(room)
print(room_up)

# Print out the number of o's in room
print(room.count("o"))


# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Print out the index of the element 20.0
print(areas.index(20.0))

# Print out how often 14.5 appears in areas
print(areas.count(14.5))


# Create list areas
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Use append twice to add poolhouse and garage size
areas.append(24.5)
areas.append(15.45)

# Print out areas
print(areas)

# Reverse the orders of the elements in areas
areas.reverse()

# Print out areas
print(areas)


# Definition of radius
r = 0.43

# Import the math package
import math

# Calculate C
C = 2 * math.pi * r

# Calculate A
A = math.pi * (r ** 2)

# Build printout
print("Circumference: " + str(C))
print("Area: " + str(A))


# Definition of radius
r = 192500

# Import radians function of math package
from math import radians

# Travel distance of Moon over 12 degrees. Store in dist.
dist = r * radians(12)

# Print out dist
print(dist)

## <class 'list'>
## 4
## [20.0, 18.0, 11.25, 10.75, 9.5]
## poolhouse
## POOLHOUSE
## 3
## 2
## 0
## [11.25, 18.0, 20.0, 10.75, 9.5, 24.5, 15.45]
## [15.45, 24.5, 9.5, 10.75, 20.0, 18.0, 11.25]
## Circumference: 2.701769682087222
## Area: 0.5808804816487527
## 40317.10572106901

---

Chapter 4 - Numpy

Numpy extends list operations using “Numerical Python” (collections of values, optimized for speed):

• The Numpy array is like a list, but you can run mathematical calculations with it
  • For example, [1, 2, 3] * 2 is [1, 2, 3, 1, 2, 3] while [1, 2, 3] **2 throws an error
    
  • However, numpy.array([1, 2, 3]) * 2 is array([2, 4, 6]) while numpy.array([1, 2, 3]) ** 2 is array([1, 4, 9]), both as expected
    
• The basic structure of numpy.array() is a vector, which will operate element-wise
  • Numpy arrays must be of a single-type, converted to the “most flexible” (e.g., string is more flexible than float is more flexible than boolean)
    
• The plus sign with a numpy.array() will add element-wise rather than pasting (as it would with lists)
  
• Can also use logical subsetting; for example, bmi[bmi > 23] will return all bmi that are greater than 23

2D Numpy Arrays - extending the vector to be multi-dimensional:

• For a numpy vector/array, the type will be numpy.ndarray (stands for n-dimensional array)
  
• Can create a two-dimensional array much like an array of lists; numpy.array( [ [1, 2, 3], [4, 5, 6] ] )
  • The .shape() method will give the dimensions of the array as rows, columns
    
• Selecting a row is just based on myArray[rowIndex], so a specific cell can be extracted with myArray[rowIndex][colIndex]
  • Alternately, myArray[rowIndex, colIndex] will also return the specified row and column
    
  • Can also use myArray[:, colIndex] to get just the specified column(s)
    
• The 2D Numpy arrays can also be used for element-wise operations
• The 2D Numpy arrays can also be used for element-wise operations

Numpy Basic Statistics - basic data exploration:

• numpy.mean() will take the mean of the relevant data
  
• numpy.median() will take the median of the relevant data
  
• numpy.corrcoef() will create the correlation coefficients
  
• numpy.std() will take the standard deviation
  
• numpy.sum() and numpy.sort() are faster than the base versions since numpy has enforced common data types within the array
  
• Note that Filip manufactured the MLB data as follows
  • height = numpy.round(numpy.random.normal(1.75, 0.20, 5000), 2)
    
  • weight = numpy.round(numpy.random.normal(60.32, 15, 5000), 2)
    
  • np_baseball = np.column_stack((height, weight))

Example code includes:

# Create list baseball
baseball = [180, 215, 210, 210, 188, 176, 209, 200]

# Import the numpy package as np
import numpy as np

# Create a Numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out type of np_baseball
print(type(np_baseball))


# DO NOT HAVE THE HEIGHT OR WEIGHT DATA - it is MLB data on 1000 players
# Create dummy data
height = np.round(np.random.normal(1.75, 0.20, 5000), 2)  
weight = np.round(np.random.normal(60.32, 15, 5000), 2)  


# Create a Numpy array from height: np_height
np_height = np.array(height)

# Print out np_height
print(np_height)

# Convert np_height to m: np_height_m
np_height_m = np_height * 0.0254

# Print np_height_m
print(np_height_m)


# Create array from height with correct units: np_height_m
np_height_m = np.array(height) * 0.0254

# Create array from weight with correct units: np_weight_kg
np_weight_kg = np.array(weight) * 0.453592

# Calculate the BMI: bmi
bmi = np_weight_kg / (np_height_m ** 2)

# Print out bmi
print(bmi)


# Calculate the BMI: bmi
np_height_m = np.array(height) * 0.0254
np_weight_kg = np.array(weight) * 0.453592
bmi = np_weight_kg / np_height_m ** 2

# Create the light array
light = bmi < 21

# Print out light
print(light)

# Print out BMIs of all baseball players whose BMI is below 21
print(bmi[light])


# Store weight and height lists as numpy arrays
np_weight = np.array(weight)
np_height = np.array(height)

# Print out the weight at index 50
print(np_weight[50])

# Print out sub-array of np_height: index 100 up to and including index 110
print(np_height[100:111])


# Create baseball, a list of lists
baseball = [[180, 78.4],
            [215, 102.7],
            [210, 98.5],
            [188, 75.2]]

# Import numpy
import numpy as np

# Create a 2D Numpy array from baseball: np_baseball
np_baseball = np.array(baseball)

# Print out the type of np_baseball
print(type(np_baseball))

# Print out the shape of np_baseball
print(np_baseball.shape)


# DO NOT HAVE baseball, which is a list of lists of the 1015 MLB players with their height/weight
# Create a 2D Numpy array from baseball: np_baseball
# np_baseball = np.array(baseball)
# Dummy up the data instead
np_baseball = np.column_stack((height, weight))  

# Print out the shape of np_baseball
print(np_baseball.shape)  # 1015 x 2


# Create np_baseball (2 cols)
# np_baseball = np.array(baseball)

# Print out the 50th row of np_baseball
print(np_baseball[49])

# Select the entire second column of np_baseball: np_weight
np_weight = np_baseball[:, 1]

# Print out height of 124th player
print(np_baseball[123, 0])


# DO NOT HAVE baseball OR updated ; each should be 1,015 x 3 (height, weight, bmi)
# Create np_baseball (3 cols)
# np_baseball = np.array(baseball)

# Print out addition of np_baseball and updated
# print(np_baseball + updated)

# Create Numpy array: conversion
# conversion = np.array([0.0254, 0.453592, 1])

# Print out product of np_baseball and conversion
# print(np_baseball * conversion)


# Create np_height from np_baseball
np_height = np_baseball[:, 0]

# Print out the mean of np_height
print(np.mean(np_height))

# Print out the median of np_height
print(np.median(np_height))


# Print mean height (first column)
avg = np.mean(np_baseball[:,0])
print("Average: " + str(avg))

# Print median height. Replace 'None'
med = np.median(np_baseball[:,0])
print("Median: " + str(med))

# Print out the standard deviation on height. Replace 'None'
stddev = np.std(np_baseball[:,0])
print("Standard Deviation: " + str(stddev))

# Print out correlation between first and second column. Replace 'None'
corr = np.corrcoef(np_baseball[:, 0], np_baseball[:, 1])
print("Correlation: " + str(corr))


# DO NOT HAVE DATA for positions or heights (soccer data . . . )
# Convert positions and heights to numpy arrays: np_positions, np_heights
# np_positions = np.array(positions)
# np_heights = np.array(heights)

# Heights of the goalkeepers: gk_heights
# gk_heights = np_heights[np_positions == "GK"]

# Heights of the other players: other_heights
# other_heights = np_heights[np_positions != "GK"]

# Print out the median height of goalkeepers. Replace 'None'
# print("Median height of goalkeepers: " + str(np.median(gk_heights)))

# Print out the median height of other players. Replace 'None'
# print("Median height of other players: " + str(np.median(other_heights)))

## <class 'numpy.ndarray'>
## [ 1.83  2.05  1.39 ...,  1.72  1.77  1.46]
## [ 0.046482  0.05207   0.035306 ...,  0.043688  0.044958  0.037084]
## [ 13251.4305197   12978.96335424  24995.50231956 ...,  12388.78694226
##   14176.28048062  15805.52954313]
## [False False False ..., False False False]
## []
## 52.17
## [ 1.73  1.62  1.56  1.58  1.79  1.59  1.37  1.65  1.15  1.53  1.82]
## <class 'numpy.ndarray'>
## (4, 2)
## (5000, 2)
## [  1.71  73.93]
## 1.83
## 1.749982
## 1.75
## Average: 1.749982
## Median: 1.75
## Standard Deviation: 0.201624352884
## Correlation: [[ 1.         0.0014307]
##  [ 0.0014307  1.       ]]

Intermediate Python for Data Science

Chapter 1 - Matplotlib for Data Visualization

Basic plots with matplotlib - generally, the heart of visualization within Python:

• Need to import the key functions; for example import matplotlib.pyplot as plt
  
• Then, plt.plot(list1, list2) will create a line plot with list1 being x and list2 being y
  • If you want to actually see the plot, use plt.show(), somewhat like plt.plot() just being a saved ggplot2 object
    
• Alternately, plt.scatter() to create a scatter plot

Histograms are useful for exploring a dataset (getting an idea about the distribution):

• import matplotlib.pyplot as plt # help(plt.hist) will show all the options for a histogram
  
• plt.hist(x, bins=myBins) # default for myBins is 10
  • Needs plt.show() as per the above

Customization for changing the base plot types in Python:

• Can label x-axis with plt.xlabel(‘X Label’)
  
• Can label y-axis with plt.xlabel(‘Y Label’)
  
• Can add title with plt.title(‘My Title’)
  
• Can add plt.yticks([myList], [myNames]) # myList can be 2+ elements which will define the y-range; optional list myNames must be the same length as myList and will label the y-axis
  • All of these must be run PRIOR to the plt.show() command

Example code includes:

# Define the reading data path
readPath = "C:/Users/Dave/Documents/Personal/Learning/Coursera/RDirectory/RHomework/DataCamp/"

# This is world population 1950-2100 (DO NOT HAVE FILE)
# Import some wikipedia data from CSV as panda
import pandas as pd

globalPop = pd.read_csv(readPath + "GlobalPopYear_1950_2100_v001.csv")

year = globalPop["year"]
pop = globalPop["pop"]

# Print the last item from year and pop
print(year.iloc[-1])
print(pop.iloc[-1])

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Make a line plot: year on the x-axis, pop on the y-axis
plt.plot(year, pop)

# Display the plot with plt.show()
# Need to use a proper Python IDE for plt.show() - otherwise just pops up the images "live"
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy001.png", bbox_inches="tight")

## 2100
## 11000000002

The population plot saved from Python is:

Next, the Hans Rosling Data is explored:

# Using the Hans Rosling Data (2007 life expectancy and GDP for 142 countries)
# Create from Wikipedia, World Bank, and the like
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# readPath = "C:\\Users\\Dave\\Documents\\Personal\\Learning\\Coursera\\RDirectory\\RHomework\\DataCamp\\"
readPath = "C:/Users/Dave/Documents/Personal/Learning/Coursera/RDirectory/RHomework/DataCamp/"


globalData = pd.read_csv(readPath + "GlobalGDPLifeExpectancy_v001.csv")

gdp_cap = 1000000 * np.array(globalData["gdp"]) / np.array(globalData["pop"])
life_exp = globalData["le_2015"]
pop = globalData["pop"]
life_exp1950 = globalData["le_1960"]  # Much easier to get 1960 than 1950 online - KLUGE
regn = globalData["region"]

# Print the last item of gdp_cap and life_exp
print(gdp_cap[-1])  # Since it is a numpy
print(life_exp.iloc[-1])  # Since it is a panda

# Make a line plot, gdp_cap on the x-axis, life_exp on the y-axis
plt.plot(gdp_cap, life_exp)

# Display the plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy002.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Change the line plot below to a scatter plot
plt.scatter(gdp_cap, life_exp)

# Put the x-axis on a logarithmic scale
plt.xscale('log')

# Show plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy003.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Brings in yet another variable, population

# Build Scatter plot
plt.scatter(pop, life_exp)
plt.xscale("log")

# Show plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy004.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Create histogram of life_exp data
plt.hist(life_exp)

# Display histogram
# Need to use a proper Python IDE for plt.show()
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy005.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Build histogram with 5 bins
plt.hist(life_exp, bins=5)

# Show and clean up plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# plt.clf()
# Save as dummy PNG instead
plt.savefig("_dummyPy006.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Build histogram with 20 bins
plt.hist(life_exp, bins=20)

# Show and clean up again
# Need to use a proper Python IDE for plt.show()
# plt.show()
# plt.clf()
# Save as dummy PNG instead
plt.savefig("_dummyPy007.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Histogram of life_exp, 15 bins
plt.hist(life_exp, bins=15)

# Show and clear plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# plt.clf()
# Save as dummy PNG instead
plt.savefig("_dummyPy008.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Histogram of life_exp1950, 15 bins
plt.hist(life_exp1950, bins=15)

# Show and clear plot again
# Need to use a proper Python IDE for plt.show()
# plt.show()
# plt.clf()
# Save as dummy PNG instead
plt.savefig("_dummyPy009.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Basic scatter plot, log scale
plt.scatter(gdp_cap, life_exp)
plt.xscale('log') 

# Strings
xlab = 'GDP per Capita [in USD]'
ylab = 'Life Expectancy [in years]'
title = 'World Development in 2007'

# Add axis labels
plt.xlabel(xlab)
plt.ylabel(ylab)

# Add title
plt.title(title)

# After customizing, display the plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy010.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Scatter plot
plt.scatter(gdp_cap, life_exp)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')

# Definition of tick_val and tick_lab
tick_val = [1000,10000,100000]
tick_lab = ['1k','10k','100k']

# Adapt the ticks on the x-axis
plt.xticks(tick_val, tick_lab)

# After customizing, display the plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy011.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Import numpy as np
import numpy as np

# Store pop as a numpy array: np_pop
np_pop = np.array(pop) / 1000000  # Population in millions

# Double np_pop
np_pop = np_pop * 2 # Doubled for larger bubbles

# Update: set s argument to np_pop
plt.scatter(gdp_cap, life_exp, s = np_pop)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000, 10000, 100000],['1k', '10k', '100k'])

# Display the plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy012.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Color is based on continent, using the below dictionary
colDict = {
    'Asia':'red',
    'Europe':'green',
    'Africa':'blue',
    'Americas':'yellow',
    'Oceania':'black'
}

col=[]

for eachRegion in regn :
    col.append(colDict[eachRegion])

# Specify c and alpha inside plt.scatter()
plt.scatter(x = gdp_cap, y = life_exp, s = np_pop , c=col, alpha=0.8)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000,10000,100000], ['1k','10k','100k'])

# Show the plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# plt.clf()
# Save as dummy PNG instead
plt.savefig("_dummyPy013.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Scatter plot
plt.scatter(x = gdp_cap, y = life_exp, s = np_pop, c = col, alpha = 0.4)

# Previous customizations
plt.xscale('log') 
plt.xlabel('GDP per Capita [in USD]')
plt.ylabel('Life Expectancy [in years]')
plt.title('World Development in 2007')
plt.xticks([1000,10000,100000], ['1k','10k','100k'])

# Additional customizations
plt.text(1550, 71, 'India')
plt.text(5700, 80, 'China')

# Add grid() call
plt.grid(True)

# Show the plot
# Need to use a proper Python IDE for plt.show()
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy014.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

## 888.906425266
## 59.2

GDP vs Life Expectancy by Country as Line Graph (not good . . . ):

GDP vs Life Expectancy by Country as Scatter Plot:

GDP vs Life Expectancy by Country as Scatter Plot with Log Scale:

Life Expectancy Histogram (default 10 bins):

Life Expectancy Histogram (5 bins):

Life Expectancy Histogram (20 bins):

Life Expectancy Histogram for 2015 (15 bins):

Life Expectancy Histogram for 1960 (15 bins):

Base Rosling-like graph (GDP vs Life Expectancy by Country Scatter):

Rosling-like graph (enhanced tick labels):

Rosling-like graph (bubble size ~ population):

Rosling-like graph (bubble color based on region):

Rosling-like graph (semit-transparent bubbles):

---

Chapter 2 - Dictionaries and Pandas

Dictionaries, Part I - key-value pairs:

• The dictionary is created with curly brackets, with key-value pairs denoted by a colon and separated by a comma
  • world = { “afghanistan”:31, “albania”:2.8, “algeria”:39 } # sets up three key-value pairs as the dictionary called world
    
  • Now, world[“albania”] will return 2.8, the value that is associated with key “albania”
    
• Dictionary look-ups are extremely fast even for enormous dictionaries

Dictionaries, Part II:

• Dictionaries need to have unique keys; if duplicate keys are included, the value associated with the LAST key is retained
  
• The keys also need to be immutable objects, which is to say strings or booleans or integers or floats (but not lists, since you can change their contents dynamically)
  
• Assigning (or changing) key-value pairs in a dictionary is myDict[myKey] = myValue
  
• To test whether a key is in the dictionary, use myKey in myDict # returns boolean True or False
  
• To delete an item from the dictionary, use del(myDict[myKey]) # the full key-value pair is removed
  
• Lists and dictionaries have many similarities, but also some key differences
  • Lists are indexed by a range of numbers, making them ideal for collections of values where the order matters
    
  • Dictionaries are indexed by unique keys, making them ideal for fast look-ups (they are also inherently completely unordered/unsorted based on how they are hashed)

Pandas, Part I - tabular dataset storage and manipulation:

• Same general philosophy where rows are observations and columns are attributes/variables
  
• Basically, need a form of numpy.array() that allows for different variable types in different columns
  
• The pandas package provides a high-level data-manipulation tool (built on numpy by Wes McKinney)
  • The pandas package conveniently stores data as a DataFrame
    
  • Generally, the rows and columns will all have unique names
    
  • Further, the columns can all be of different types
    
• Suppose that you create a dictionary where the keys are the desired column labels while the values are a list of the desired values for the column
  • import pandas as pd
    
  • myFrame = pd.DataFrame(myDict)
    
  • myFrame.index = labelList # optional, if wanting to provide row-names
    
• Alternately, the data can be imported such as from a CSV
  • pd.read_csv(myCSVPath, index_col=myIndex) # index_col is optional and needed only if an index column has been provided

Pandas, Part II - indexing and selecting data from a DataFrame using square brackets, loc, and iloc:

• myFrame[colNameQuoted] will return a subset of the panda with type pandas.core.series.Series
  
• myFrame[[colNameQuoted]] will return a single-column panda with type pandas.core.frame.DataFrame
  
• myFrame[[colName1Quoted, colName2Quoted]] will return a two-column panda
  
• myFrame[a:b] will return rows rather than columns, starting with index a and ending at index b-1
  
• The loc and iloc tools are designed to extend Pandas data extraction to be more similar to numpy extractions such as [ rows, columns ]
  • myFrame.loc[rowNameQuoted] will return a panda series matching the ROW
    
  • myFrame.loc[[rowNameQuoted]] will return a panda frame containing just that ROW
    
  • myFrame.loc[[rowName1Quoted, rowName2Quoted, rowName3Quoted]] will return a panda frame containing the requested ROWS
    
  • myFrame.loc[[rowListQuoted], [colListQuoted]] will return just the specified rows and columns
    
  • myFrame.loc[:, [colListQuoted]] will return all rows and just the specified columns
    
• The iloc function is the index-based version of loc for data access and extraction
  • myFrame.iloc[[rowIndices]] will return a panda frame containing just these ROWS
    
  • myFrame.iloc[[rowIndices], [colIndices]] will return a panda frame containing just these COLUMNS

Example code includes:

# Definition of countries and capital
countries = ['spain', 'france', 'germany', 'norway']
capitals = ['madrid', 'paris', 'berlin', 'oslo']

# Get index of 'germany': ind_ger
ind_ger = countries.index("germany")

# Use ind_ger to print out capital of Germany
print(capitals[ind_ger])


# Definition of countries and capital
countries = ['spain', 'france', 'germany', 'norway']
capitals = ['madrid', 'paris', 'berlin', 'oslo']

# From string in countries and capitals, create dictionary europe
europe = {
   'spain':'madrid', 
   'france':'paris', 
   'germany':'berlin', 
   'norway':'oslo'
}

# Print europe
print(europe)


# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }

# Print out the keys in europe
print(europe.keys())

# Print out value that belongs to key 'norway'
print(europe['norway'])


# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'berlin', 'norway':'oslo' }

# Add italy to europe
europe['italy'] = 'rome'

# Print out italy in europe
print('italy' in europe)

# Add poland to europe
europe['poland'] = 'warsaw'

# Print europe
print(europe)


# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn',
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw',
          'australia':'vienna' }

# Update capital of germany
europe['germany'] = 'berlin'

# Remove australia
del(europe['australia'])

# Print europe
print(europe)


# Dictionary of dictionaries
europe = { 'spain': { 'capital':'madrid', 'population':46.77 },
           'france': { 'capital':'paris', 'population':66.03 },
           'germany': { 'capital':'berlin', 'population':80.62 },
           'norway': { 'capital':'oslo', 'population':5.084 } }


# Print out the capital of France
print(europe['france']['capital'])

# Create sub-dictionary data
data = { 'capital':'rome', 'population':59.83 }

# Add data to europe under key 'italy'
europe['italy'] = data

# Print europe
print(europe)


# Pre-defined lists
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]

# Import pandas as pd
import pandas as pd

# Create dictionary my_dict with three key:value pairs: my_dict
my_dict = { 'country': names, 'drives_right': dr, 'cars_per_cap': cpc }

# Build a DataFrame cars from my_dict: cars
cars = pd.DataFrame(my_dict)

# Print cars
print(cars)


# Build cars DataFrame
names = ['United States', 'Australia', 'Japan', 'India', 'Russia', 'Morocco', 'Egypt']
dr =  [True, False, False, False, True, True, True]
cpc = [809, 731, 588, 18, 200, 70, 45]
dict = { 'country':names, 'drives_right':dr, 'cars_per_cap':cpc }
cars = pd.DataFrame(dict)
print(cars)

# Definition of row_labels
row_labels = ['US', 'AUS', 'JAP', 'IN', 'RU', 'MOR', 'EG']

# Specify row labels of cars
cars.index = row_labels

# Print cars again
print(cars)


# DO NOT HAVE FILE "cars.csv" - cars_per_cap , country , drives_right
# Created as cars.to_csv("cars.csv")
# Import the cars.csv data: cars
cars = pd.read_csv("cars.csv")

# Print out cars
print(cars)


# SLIGHTLY DIFFERENT VERSION WITH ROW NAMES AS THE FIRST COLUMN
# Import pandas as pd
import pandas as pd

# Fix import by including index_col
cars = pd.read_csv('cars.csv', index_col=0)

# Print out cars
print(cars)


# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out country column as Pandas Series
print(cars["country"])

# Print out country column as Pandas DataFrame
print(cars[["country"]])

# Print out DataFrame with country and drives_right columns
print(cars[["country", "drives_right"]])


# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Print out first 3 observations
print(cars[0:3])

# Print out fourth, fifth and sixth observation
print(cars[3:6])


# Print out observation for Japan
print(cars.loc["JAP"])

# Print out observations for Australia and Egypt
print(cars.loc[["AUS", "EG"]])


# Print out drives_right value of Morocco
print(cars.loc[["MOR"], ["drives_right"]])

# Print sub-DataFrame
print(cars.loc[["RU", "MOR"], ["country", "drives_right"]])


# Print out drives_right column as Series
print(cars.loc[:, "drives_right"])

# Print out drives_right column as DataFrame
print(cars.loc[:, ["drives_right"]])

# Print out cars_per_cap and drives_right as DataFrame
print(cars.loc[:, ["cars_per_cap", "drives_right"]])

## berlin
## {'spain': 'madrid', 'france': 'paris', 'germany': 'berlin', 'norway': 'oslo'}
## dict_keys(['spain', 'france', 'germany', 'norway'])
## oslo
## True
## {'spain': 'madrid', 'france': 'paris', 'germany': 'berlin', 'norway': 'oslo', 'italy': 'rome', 'poland': 'warsaw'}
## {'spain': 'madrid', 'france': 'paris', 'germany': 'berlin', 'norway': 'oslo', 'italy': 'rome', 'poland': 'warsaw'}
## paris
## {'spain': {'capital': 'madrid', 'population': 46.77}, 'france': {'capital': 'paris', 'population': 66.03}, 'germany': {'capital': 'berlin', 'population': 80.62}, 'norway': {'capital': 'oslo', 'population': 5.084}, 'italy': {'capital': 'rome', 'population': 59.83}}
##    cars_per_cap        country  drives_right
## 0           809  United States          True
## 1           731      Australia         False
## 2           588          Japan         False
## 3            18          India         False
## 4           200         Russia          True
## 5            70        Morocco          True
## 6            45          Egypt          True
##    cars_per_cap        country  drives_right
## 0           809  United States          True
## 1           731      Australia         False
## 2           588          Japan         False
## 3            18          India         False
## 4           200         Russia          True
## 5            70        Morocco          True
## 6            45          Egypt          True
##      cars_per_cap        country  drives_right
## US            809  United States          True
## AUS           731      Australia         False
## JAP           588          Japan         False
## IN             18          India         False
## RU            200         Russia          True
## MOR            70        Morocco          True
## EG             45          Egypt          True
##   Unnamed: 0  cars_per_cap        country  drives_right
## 0         US           809  United States          True
## 1        AUS           731      Australia         False
## 2        JAP           588          Japan         False
## 3         IN            18          India         False
## 4         RU           200         Russia          True
## 5        MOR            70        Morocco          True
## 6         EG            45          Egypt          True
##      cars_per_cap        country  drives_right
## US            809  United States          True
## AUS           731      Australia         False
## JAP           588          Japan         False
## IN             18          India         False
## RU            200         Russia          True
## MOR            70        Morocco          True
## EG             45          Egypt          True
## US     United States
## AUS        Australia
## JAP            Japan
## IN             India
## RU            Russia
## MOR          Morocco
## EG             Egypt
## Name: country, dtype: object
##            country
## US   United States
## AUS      Australia
## JAP          Japan
## IN           India
## RU          Russia
## MOR        Morocco
## EG           Egypt
##            country  drives_right
## US   United States          True
## AUS      Australia         False
## JAP          Japan         False
## IN           India         False
## RU          Russia          True
## MOR        Morocco          True
## EG           Egypt          True
##      cars_per_cap        country  drives_right
## US            809  United States          True
## AUS           731      Australia         False
## JAP           588          Japan         False
##      cars_per_cap  country  drives_right
## IN             18    India         False
## RU            200   Russia          True
## MOR            70  Morocco          True
## cars_per_cap      588
## country         Japan
## drives_right    False
## Name: JAP, dtype: object
##      cars_per_cap    country  drives_right
## AUS           731  Australia         False
## EG             45      Egypt          True
##      drives_right
## MOR          True
##      country  drives_right
## RU    Russia          True
## MOR  Morocco          True
## US      True
## AUS    False
## JAP    False
## IN     False
## RU      True
## MOR     True
## EG      True
## Name: drives_right, dtype: bool
##      drives_right
## US           True
## AUS         False
## JAP         False
## IN          False
## RU           True
## MOR          True
## EG           True
##      cars_per_cap  drives_right
## US            809          True
## AUS           731         False
## JAP           588         False
## IN             18         False
## RU            200          True
## MOR            70          True
## EG             45          True

---

Chapter 3 - Logic, Control Flow, and Filtering

Comparison Operators - how two values relate (tests for equality, greater, lesser, etc.):

• Less than (<), greater than (>), equals (==), less than or equal (<=), greater than or equal (>=), and not equals (!=) are as per R
  
• Need to have comparisons between objects of the same type (specifically, not comparing strings and floats)

Boolean operators - most commonly used are and, or, and not:

• In Python, the word “and” is used rather than & or &&
  
• In Python, the word “or” is used rather than | or ||
  
• In Python, the word “not” is used rather than - or !
  
• If comparisons will be run on an array, then use np.logical_and(), np.logical_or(), and np.logical_not()
  • np.logical_and(bmi > 27, bmi < 30)

If, elif, else:

• General syntax is “if condition : action” followed optionally by “elif condition : action” or “else condition : action”
  • If written on multiple lines, the action should be indented by 4 spaces and may include block instructions
    
  • Any code without the indentation will be known to no longer be part of the if block

Filtering Pandas DataFrame - generally a three-step process of 1) select key column as panda.series, 2) run test, and 3) use to grab relevant rows:

• If you pass myFrame[myBool] where myBool is the same size (number of rows) as myFrame, then it will automatically pull back the rows where myBool == True
  
• Because pandas are built on the numpy infrastructure, np.logical_and() and the related terms will work on the pandas also

Example code includes:

# Comparison of booleans
print(True == False)

# Comparison of integers
print((-5 * 15) != 75)

# Comparison of strings
print("pyscript" == "PyScript")

# Compare a boolean with an integer
print(True == 1)


# Comparison of integers
x = -3 * 6
print(x >= -10)

# Comparison of strings
y = "test"
print("test" <= y)

# Comparison of booleans
print(True > False)


# Create arrays
import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50])
your_house = np.array([14.0, 24.0, 14.25, 9.0])

# my_house greater than or equal to 18
print(my_house >= 18)

# my_house less than your_house
print(my_house < your_house)


# Define variables
my_kitchen = 18.0
your_kitchen = 14.0

# my_kitchen bigger than 10 and smaller than 18?
print(my_kitchen > 10 and my_kitchen < 18)

# my_kitchen smaller than 14 or bigger than 17?
print(my_kitchen < 14 or my_kitchen > 17)

# Double my_kitchen smaller than triple your_kitchen?
print(2 * my_kitchen < 3 * your_kitchen)


# Create arrays
import numpy as np
my_house = np.array([18.0, 20.0, 10.75, 9.50])
your_house = np.array([14.0, 24.0, 14.25, 9.0])

# my_house greater than 18.5 or smaller than 10
print(np.logical_or(my_house > 18.5, my_house < 10))

# Both my_house and your_house smaller than 11
print(np.logical_and(my_house <11, your_house < 11))


# Define variables
room = "kit"
area = 14.0

# if statement for room
if room == "kit" :
    print("looking around in the kitchen.")

# if statement for area
if area > 15 :
    print("big place!")


# Define variables
room = "kit"
area = 14.0

# if-else construct for room
if room == "kit" :
    print("looking around in the kitchen.")
else :
    print("looking around elsewhere.")

# if-else construct for area
if area > 15 :
    print("big place!")
else :
    print("pretty small.")


# Define variables
room = "bed"
area = 14.0

# if-elif-else construct for room
if room == "kit" :
    print("looking around in the kitchen.")
elif room == "bed":
    print("looking around in the bedroom.")
else :
    print("looking around elsewhere.")

# if-elif-else construct for area
if area > 15 :
    print("big place!")
elif area > 10 :
    print("medium size, nice!")
else :
    print("pretty small.")


# AS PER ABOVE, DO NOT HAVE THIS DATASET
# That has since been worked around . . . 
# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Extract drives_right column as Series: dr
dr = cars["drives_right"]

# Use dr to subset cars: sel
sel = cars[dr]

# Print sel
print(sel)


# Convert code to a one-liner
sel = cars[cars['drives_right']]

# Print sel
print(sel)


# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Create car_maniac: observations that have a cars_per_cap over 500
cpc = cars["cars_per_cap"]
many_cars = cpc > 500
car_maniac = cars[many_cars]

# Print car_maniac
print(car_maniac)


# Create medium: observations with cars_per_cap between 100 and 500
cpc = cars['cars_per_cap']
between = np.logical_and(cpc > 100, cpc < 500)
medium = cars[between]

# Print medium
print(medium)

## False
## True
## False
## True
## False
## True
## True
## [ True  True False False]
## [False  True  True False]
## False
## True
## True
## [False  True False  True]
## [False False False  True]
## looking around in the kitchen.
## looking around in the kitchen.
## pretty small.
## looking around in the bedroom.
## medium size, nice!
##      cars_per_cap        country  drives_right
## US            809  United States          True
## RU            200         Russia          True
## MOR            70        Morocco          True
## EG             45          Egypt          True
##      cars_per_cap        country  drives_right
## US            809  United States          True
## RU            200         Russia          True
## MOR            70        Morocco          True
## EG             45          Egypt          True
##      cars_per_cap        country  drives_right
## US            809  United States          True
## AUS           731      Australia         False
## JAP           588          Japan         False
##     cars_per_cap country  drives_right
## RU           200  Russia          True

---

Chapter 4 - Loops

The while loop - alternative to the if/elif/else process:

• The while loop will continue to execute as long as the condition is met
  
• These loops are typically rare (and can easily cause an infinite loop), but can be powerful in certain circumstances
  
• The syntax is while condition : expression # If expression is placed on the next line(s), then it should be indented by 4 spaces
  
• CTRL-C will typically kill a Python infinte loop

The for loop - alternative to the while loop:

• The basic syntax is for var in seq : expression # as per previous, if expression is on the next line(s), it should be indented by 4 spaces
  • The seq can be a list or a dictionary or the like, which will iterate by item in the list or disctionary or the like
    
• Using a list, the enumerate() command will pull out a tuple which can be used in the iterations
  for a, b in enumerate(myList) : expression # a will be the index and b will be the value
  
• If iterating over a string, the for loop will extract character by character

Looping data structures - Part I - extension to dictionaries, numpy arrays, and the like:

• Looping through a dictionary requires calling the .items() method on the dictionary
  • for key, value in myDict.items() : expression # will extract key, value as tuples
    
  • Since dictionaries are unordered, the key will not be sorted and can come out in any order (hash-table dependent)
    
• Looping through a 1-D numpy array will work the same as looping through a list; standard for-loop syntax
  
• Looping through a 2-D numpy array will extract the 1-D numpy arrays underlying the 2-D numpy array (which may or may not be the desired output)
  • Alternately, using np.nditer(myNumpy2D) will extract the items one at a time

Looping data structures - Part II - extension to pandas DataFrame:

• The basic expression for x in myPanda : expression # the x will just iterate across the column names
  
• To extract the rows, use for lab, row in myPanda.iterrows() : expression # the lab will be the row name and the row will be the row data, iterated over all the rows
  
• Rather than using a loop, the apply function can be used to create new columns in the panda
  • myPanda[“myNewCol”] = myPanda[“myOldCol”].apply(len) # will create new variable myNewCol as len(myOldCol)

Example code includes:

# Initialize offset
offset = 8

# Code the while loop
while offset != 0 :
    print("correcting...")
    offset = offset - 1
    print(offset)


# Initialize offset
offset = -6

# Code the while loop
while offset != 0 :
    print("correcting...")
    if offset > 0 :
        offset = offset - 1
    else :
        offset = offset + 1
    print(offset)


# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Code the for loop
for x in areas :
    print(x)


# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Change for loop to use enumerate()
for a, b in enumerate(areas) :
    print("room " + str(a) + ": " + str(b))


# areas list
areas = [11.25, 18.0, 20.0, 10.75, 9.50]

# Code the for loop
for index, area in enumerate(areas) :
    print("room " + str(index + 1) + ": " + str(area))


# house list of lists
house = [["hallway", 11.25], 
         ["kitchen", 18.0], 
         ["living room", 20.0], 
         ["bedroom", 10.75], 
         ["bathroom", 9.50]]
         
# Build a for loop from scratch
for rooms in house :
    print("the " + str(rooms[0]) + " is " + str(rooms[1]) + " sqm")


# Definition of dictionary
europe = {'spain':'madrid', 'france':'paris', 'germany':'bonn', 
          'norway':'oslo', 'italy':'rome', 'poland':'warsaw', 'australia':'vienna' }
          
# Iterate over europe
for country, capital in europe.items() :
    print("the capital of " + str(country) + " is " + str(capital))


# Import numpy as np
import numpy as np

# DO NOT HAVE EITHER DATASET
# Create np_height
height = np.round(np.random.normal(1.75, 0.20, 50), 2)  
np_height = np.array(height)

# Create np_baseball
# baseball = [180, 215, 210, 210, 188, 176, 209, 200]
# np_baseball = np.array(baseball)

weight = np.round(np.random.normal(60.32, 15, 50), 2)
np_baseball = np.column_stack((height, weight))


# For loop over np_height
for height in np_height :
    print(str(height) + " inches")

# The end= argument over-rides the default to move to a new line
# For loop over np_baseball
for item in np.nditer(np_baseball) :
    print(item, end=" ")


# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Iterate over rows of cars
for lab, dat in cars.iterrows() :
    print(lab)
    print(dat)


# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Adapt for loop
for lab, row in cars.iterrows() :
    print(lab + ": " + str(row['cars_per_cap']))


# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Code for loop that adds COUNTRY column
for lab, row in cars.iterrows() :
    cars.loc[lab, "COUNTRY"] = row['country'].upper()

# Print cars
print(cars)


# Import cars data
import pandas as pd
cars = pd.read_csv('cars.csv', index_col = 0)

# Use .apply(str.upper)
cars["COUNTRY"] = cars["country"].apply(str.upper)
print(cars)

## correcting...
## 7
## correcting...
## 6
## correcting...
## 5
## correcting...
## 4
## correcting...
## 3
## correcting...
## 2
## correcting...
## 1
## correcting...
## 0
## correcting...
## -5
## correcting...
## -4
## correcting...
## -3
## correcting...
## -2
## correcting...
## -1
## correcting...
## 0
## 11.25
## 18.0
## 20.0
## 10.75
## 9.5
## room 0: 11.25
## room 1: 18.0
## room 2: 20.0
## room 3: 10.75
## room 4: 9.5
## room 1: 11.25
## room 2: 18.0
## room 3: 20.0
## room 4: 10.75
## room 5: 9.5
## the hallway is 11.25 sqm
## the kitchen is 18.0 sqm
## the living room is 20.0 sqm
## the bedroom is 10.75 sqm
## the bathroom is 9.5 sqm
## the capital of spain is madrid
## the capital of france is paris
## the capital of germany is bonn
## the capital of norway is oslo
## the capital of italy is rome
## the capital of poland is warsaw
## the capital of australia is vienna
## 1.91 inches
## 1.54 inches
## 1.65 inches
## 2.05 inches
## 1.87 inches
## 1.79 inches
## 2.28 inches
## 1.59 inches
## 1.79 inches
## 1.58 inches
## 1.51 inches
## 1.91 inches
## 1.91 inches
## 1.61 inches
## 1.77 inches
## 1.86 inches
## 1.93 inches
## 1.67 inches
## 1.56 inches
## 1.95 inches
## 1.65 inches
## 1.54 inches
## 1.96 inches
## 2.07 inches
## 1.7 inches
## 2.06 inches
## 1.55 inches
## 1.5 inches
## 1.81 inches
## 1.91 inches
## 1.49 inches
## 1.83 inches
## 1.8 inches
## 1.85 inches
## 1.37 inches
## 1.7 inches
## 1.73 inches
## 1.58 inches
## 1.66 inches
## 2.09 inches
## 1.85 inches
## 1.66 inches
## 1.67 inches
## 1.49 inches
## 1.89 inches
## 1.53 inches
## 1.17 inches
## 1.69 inches
## 1.69 inches
## 1.5 inches
## 1.91 61.94 1.54 64.34 1.65 58.31 2.05 54.96 1.87 47.17 1.79 64.22 2.28 32.78 1.59 50.95 1.79 78.31 1.58 58.49 1.51 62.14 1.91 53.76 1.91 71.55 1.61 67.4 1.77 55.42 1.86 51.53 1.93 40.27 1.67 50.64 1.56 44.77 1.95 82.99 1.65 47.83 1.54 49.98 1.96 60.09 2.07 49.77 1.7 62.17 2.06 35.91 1.55 87.76 1.5 73.66 1.81 77.77 1.91 44.7 1.49 54.83 1.83 59.72 1.8 46.89 1.85 49.34 1.37 39.15 1.7 64.51 1.73 63.04 1.58 51.03 1.66 64.09 2.09 53.36 1.85 65.22 1.66 61.13 1.67 53.81 1.49 53.73 1.89 59.29 1.53 62.26 1.17 73.3 1.69 63.07 1.69 61.85 1.5 59.54 US
## cars_per_cap              809
## country         United States
## drives_right             True
## Name: US, dtype: object
## AUS
## cars_per_cap          731
## country         Australia
## drives_right        False
## Name: AUS, dtype: object
## JAP
## cars_per_cap      588
## country         Japan
## drives_right    False
## Name: JAP, dtype: object
## IN
## cars_per_cap       18
## country         India
## drives_right    False
## Name: IN, dtype: object
## RU
## cars_per_cap       200
## country         Russia
## drives_right      True
## Name: RU, dtype: object
## MOR
## cars_per_cap         70
## country         Morocco
## drives_right       True
## Name: MOR, dtype: object
## EG
## cars_per_cap       45
## country         Egypt
## drives_right     True
## Name: EG, dtype: object
## US: 809
## AUS: 731
## JAP: 588
## IN: 18
## RU: 200
## MOR: 70
## EG: 45
##      cars_per_cap        country  drives_right        COUNTRY
## US            809  United States          True  UNITED STATES
## AUS           731      Australia         False      AUSTRALIA
## JAP           588          Japan         False          JAPAN
## IN             18          India         False          INDIA
## RU            200         Russia          True         RUSSIA
## MOR            70        Morocco          True        MOROCCO
## EG             45          Egypt          True          EGYPT
##      cars_per_cap        country  drives_right        COUNTRY
## US            809  United States          True  UNITED STATES
## AUS           731      Australia         False      AUSTRALIA
## JAP           588          Japan         False          JAPAN
## IN             18          India         False          INDIA
## RU            200         Russia          True         RUSSIA
## MOR            70        Morocco          True        MOROCCO
## EG             45          Egypt          True          EGYPT

---

Chapter 5 - Case Study: Hacker Statistics

Random numbers - random walk using a 6-sided dice where 1/2 means -1, 3/4/5 means +1, and 6 means roll again and go up the number of the next roll:

• Further, set a floor of step 0 and also add a 0.1% chance of falling down the stairs (presumably reverting to floor 0) at any given move
  
• Assume that this is a 100-move game, and assess the odds of ending at floor 60+
  
• Hacker statistics is simulating the game to assess the probabilities, as opposed to solving the game analytically
  
• The np.random.rand() function will return a random number between 0 and 1
  • Calling np.random.seed(mySeed) will set the seed for the upcoming trials, useful for reproducibility
    
  • Calling np.random.int(a, b) will generate random integers between a and b-1 (both inclusive) assuming equal probabilities

Random walk - well-known pattern in science:

• Initializing an empty list can be done with the square brackets; myEmptyList = []
  • Appending items to the list can be done with myEmptyList.append(myEntry)
    
• For the random walk, start at 0 making myList = [0]
  • Then, can run for x in range(runs) : myList.append(myList[x] + myRandom)
    
  • Note that range(x) will generate an integer list from 0 to x-1

Distribution of random walks - expanding on the 100-trial random walk:

• Simulating many times allows for building a distribution and then making calculations based on that distribution

Example code includes:

# Import numpy as np
import numpy as np

# Set the seed
np.random.seed(123)

# Generate and print random float
print(np.random.rand())


# Import numpy and set seed
import numpy as np
np.random.seed(123)

# Use randint() to simulate a dice
print(np.random.randint(1, 7))

# Use randint() again
print(np.random.randint(1, 7))


# Import numpy and set seed
import numpy as np
np.random.seed(123)

# Starting step
step = 50

# Roll the dice
dice = np.random.randint(1, 7)

# Finish the control construct
if dice <= 2 :
    step = step - 1
elif dice < 6 :
    step = step + 1
else :
    step = step + np.random.randint(1,7)

# Print out dice and step
print(dice)
print(step)


# Import numpy and set seed
import numpy as np
np.random.seed(123)

# Initialize random_walk
random_walk = [0]

# Complete the ___
for x in range(100) :
    # Set step: last element in random_walk
    step = random_walk[-1]

    # Roll the dice
    dice = np.random.randint(1,7)

    # Determine next step
    if dice <= 2:
        step = step - 1
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    # append next_step to random_walk
    random_walk.append(step)

# Print random_walk
print(random_walk)


# Import numpy and set seed
import numpy as np
np.random.seed(123)

# Initialize random_walk
random_walk = [0]

for x in range(100) :
    step = random_walk[-1]
    dice = np.random.randint(1,7)

    if dice <= 2:
        # Replace below: use max to make sure step can't go below 0
        step = max(0, step - 1)
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    random_walk.append(step)

print(random_walk)


# Initialization
import numpy as np
np.random.seed(123)
random_walk = [0]

for x in range(100) :
    step = random_walk[-1]
    dice = np.random.randint(1,7)

    if dice <= 2:
        step = max(0, step - 1)
    elif dice <= 5:
        step = step + 1
    else:
        step = step + np.random.randint(1,7)

    random_walk.append(step)

# Import matplotlib.pyplot as plt
import matplotlib.pyplot as plt

# Plot random_walk
plt.plot(random_walk)

# Show the plot
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy015.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting


# Initialization
import numpy as np
np.random.seed(123)

# Initialize all_walks
all_walks = []

# Simulate random walk 10 times
for i in range(10) :

    # Code from before
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)

        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
        random_walk.append(step)

    # Append random_walk to all_walks
    all_walks.append(random_walk)

# Print all_walks
print(all_walks)


import matplotlib.pyplot as plt
import numpy as np
np.random.seed(123)
all_walks = []
for i in range(10) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
        random_walk.append(step)
    all_walks.append(random_walk)

# Convert all_walks to Numpy array: np_aw
np_aw = np.array(all_walks)

# Plot np_aw and show
plt.plot(np_aw)
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy016.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting


# Transpose np_aw: np_aw_t
np_aw_t = np.transpose(np_aw)

# Plot np_aw_t and show
plt.plot(np_aw_t)
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy017.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting


import matplotlib.pyplot as plt
import numpy as np
np.random.seed(123)
all_walks = []

# Simulate random walk 250 times
for i in range(250) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)

        # Implement clumsiness
        if np.random.rand() <= 0.001 :
            step = 0

        random_walk.append(step)
    all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))
plt.plot(np_aw_t)
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy018.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting


import matplotlib.pyplot as plt
import numpy as np
np.random.seed(123)
all_walks = []

# Simulate random walk 500 times
for i in range(500) :
    random_walk = [0]
    for x in range(100) :
        step = random_walk[-1]
        dice = np.random.randint(1,7)
        if dice <= 2:
            step = max(0, step - 1)
        elif dice <= 5:
            step = step + 1
        else:
            step = step + np.random.randint(1,7)
        if np.random.rand() <= 0.001 :
            step = 0
        random_walk.append(step)
    all_walks.append(random_walk)

# Create and plot np_aw_t
np_aw_t = np.transpose(np.array(all_walks))

# Select last row from np_aw_t: ends
ends = np_aw_t[-1]

# Plot histogram of ends, display plot
plt.hist(ends)
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy019.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

## 0.6964691855978616
## 6
## 3
## 6
## 53
## [0, 3, 4, 5, 4, 5, 6, 7, 6, 5, 4, 3, 2, 1, 0, -1, 0, 5, 4, 3, 4, 3, 4, 5, 6, 7, 8, 7, 8, 7, 8, 9, 10, 11, 10, 14, 15, 14, 15, 14, 15, 16, 17, 18, 19, 20, 21, 24, 25, 26, 27, 32, 33, 37, 38, 37, 38, 39, 38, 39, 40, 42, 43, 44, 43, 42, 43, 44, 43, 42, 43, 44, 46, 45, 44, 45, 44, 45, 46, 47, 49, 48, 49, 50, 51, 52, 53, 52, 51, 52, 51, 52, 53, 52, 55, 56, 57, 58, 57, 58, 59]
## [0, 3, 4, 5, 4, 5, 6, 7, 6, 5, 4, 3, 2, 1, 0, 0, 1, 6, 5, 4, 5, 4, 5, 6, 7, 8, 9, 8, 9, 8, 9, 10, 11, 12, 11, 15, 16, 15, 16, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 28, 33, 34, 38, 39, 38, 39, 40, 39, 40, 41, 43, 44, 45, 44, 43, 44, 45, 44, 43, 44, 45, 47, 46, 45, 46, 45, 46, 47, 48, 50, 49, 50, 51, 52, 53, 54, 53, 52, 53, 52, 53, 54, 53, 56, 57, 58, 59, 58, 59, 60]
## [[0, 3, 4, 5, 4, 5, 6, 7, 6, 5, 4, 3, 2, 1, 0, 0, 1, 6, 5, 4, 5, 4, 5, 6, 7, 8, 9, 8, 9, 8, 9, 10, 11, 12, 11, 15, 16, 15, 16, 15, 16, 17, 18, 19, 20, 21, 22, 25, 26, 27, 28, 33, 34, 38, 39, 38, 39, 40, 39, 40, 41, 43, 44, 45, 44, 43, 44, 45, 44, 43, 44, 45, 47, 46, 45, 46, 45, 46, 47, 48, 50, 49, 50, 51, 52, 53, 54, 53, 52, 53, 52, 53, 54, 53, 56, 57, 58, 59, 58, 59, 60], [0, 4, 3, 2, 4, 3, 4, 6, 7, 8, 13, 12, 13, 14, 15, 16, 17, 16, 21, 22, 23, 24, 23, 22, 21, 20, 19, 20, 21, 22, 28, 27, 26, 25, 26, 27, 28, 27, 28, 29, 28, 33, 34, 33, 32, 31, 30, 31, 30, 29, 31, 32, 35, 36, 38, 39, 40, 41, 40, 39, 40, 41, 42, 43, 42, 43, 44, 45, 48, 49, 50, 49, 50, 49, 50, 51, 52, 56, 55, 54, 55, 56, 57, 56, 57, 56, 57, 59, 64, 63, 64, 65, 66, 67, 68, 69, 68, 69, 70, 71, 73], [0, 2, 1, 2, 3, 6, 5, 6, 5, 6, 7, 8, 7, 8, 7, 8, 9, 11, 10, 9, 10, 11, 10, 12, 13, 14, 15, 16, 17, 18, 17, 18, 19, 24, 25, 24, 23, 22, 21, 22, 23, 24, 29, 30, 29, 30, 31, 32, 33, 34, 35, 34, 33, 34, 33, 39, 38, 39, 38, 39, 38, 39, 43, 47, 49, 51, 50, 51, 53, 52, 58, 59, 61, 62, 61, 62, 63, 64, 63, 64, 65, 66, 68, 67, 66, 67, 73, 78, 77, 76, 80, 81, 82, 83, 85, 84, 85, 84, 85, 84, 83], [0, 6, 5, 6, 7, 8, 9, 10, 11, 12, 13, 12, 13, 12, 11, 12, 11, 12, 11, 12, 13, 17, 18, 17, 23, 22, 21, 22, 21, 20, 21, 20, 24, 23, 24, 23, 24, 23, 24, 26, 25, 24, 23, 24, 23, 28, 29, 30, 29, 28, 29, 28, 29, 28, 33, 34, 33, 32, 31, 30, 31, 32, 36, 42, 43, 44, 45, 46, 45, 46, 48, 49, 50, 51, 50, 49, 50, 49, 50, 51, 52, 51, 52, 53, 54, 53, 52, 53, 54, 59, 60, 61, 66, 65, 66, 65, 66, 67, 68, 69, 68], [0, 6, 5, 6, 5, 4, 5, 9, 10, 11, 12, 13, 12, 11, 10, 9, 8, 9, 10, 11, 12, 13, 14, 13, 14, 15, 14, 15, 16, 19, 18, 19, 18, 19, 22, 23, 24, 25, 24, 23, 26, 27, 28, 29, 28, 27, 28, 31, 32, 37, 38, 37, 38, 37, 38, 37, 43, 42, 41, 42, 44, 43, 42, 41, 42, 43, 44, 45, 49, 54, 55, 56, 57, 60, 61, 62, 63, 64, 65, 66, 65, 64, 65, 66, 65, 71, 70, 71, 72, 71, 70, 71, 70, 69, 75, 74, 73, 74, 75, 74, 73], [0, 0, 0, 1, 7, 8, 11, 12, 18, 19, 20, 26, 25, 31, 30, 31, 32, 33, 32, 38, 39, 38, 39, 38, 39, 38, 39, 38, 39, 43, 44, 46, 45, 46, 45, 44, 45, 44, 45, 44, 48, 52, 51, 50, 49, 50, 51, 55, 56, 57, 61, 60, 59, 58, 59, 60, 62, 61, 60, 61, 62, 64, 67, 72, 73, 72, 73, 74, 75, 76, 77, 76, 77, 78, 84, 83, 88, 87, 91, 90, 94, 93, 96, 97, 96, 97, 103, 102, 101, 100, 104, 103, 102, 103, 104, 103, 104, 105, 106, 107, 106], [0, 0, 0, 1, 0, 0, 4, 5, 7, 11, 17, 16, 15, 16, 17, 18, 17, 18, 17, 18, 19, 18, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 33, 32, 35, 36, 35, 34, 35, 36, 37, 36, 35, 34, 33, 34, 35, 36, 37, 38, 39, 40, 39, 40, 41, 43, 42, 43, 44, 47, 49, 50, 49, 48, 47, 46, 45, 46, 45, 46, 48, 49, 50, 49, 50, 49, 48, 49, 48, 47, 46, 47, 46, 45, 46, 47, 48, 50, 51, 52, 51, 50, 51, 57, 56, 57, 58, 63, 62, 63], [0, 0, 1, 2, 1, 2, 3, 9, 10, 11, 12, 11, 13, 14, 15, 16, 15, 16, 17, 18, 19, 18, 19, 18, 19, 20, 19, 20, 24, 25, 28, 29, 33, 34, 33, 34, 35, 34, 33, 38, 39, 40, 39, 38, 39, 40, 41, 40, 44, 43, 44, 45, 46, 47, 48, 49, 50, 49, 48, 47, 48, 49, 53, 54, 53, 54, 55, 54, 60, 61, 62, 63, 62, 63, 64, 67, 66, 67, 66, 65, 64, 65, 66, 68, 69, 70, 74, 75, 74, 73, 74, 75, 74, 73, 74, 75, 76, 75, 74, 75, 76], [0, 1, 0, 1, 2, 1, 0, 0, 1, 2, 3, 4, 5, 10, 14, 13, 14, 13, 12, 11, 12, 11, 12, 13, 12, 16, 17, 16, 17, 16, 15, 16, 15, 19, 20, 21, 22, 23, 24, 23, 24, 25, 26, 27, 28, 27, 32, 33, 34, 33, 34, 33, 34, 35, 34, 35, 40, 41, 42, 41, 42, 43, 44, 43, 44, 43, 44, 45, 44, 43, 42, 43, 44, 43, 42, 41, 42, 46, 47, 48, 49, 50, 51, 50, 51, 52, 51, 52, 57, 58, 57, 56, 57, 56, 55, 54, 58, 59, 60, 61, 60], [0, 1, 2, 3, 4, 5, 4, 3, 6, 5, 4, 3, 2, 3, 9, 10, 9, 10, 11, 10, 9, 10, 11, 12, 11, 15, 16, 15, 17, 18, 17, 18, 19, 20, 21, 22, 23, 22, 21, 22, 23, 22, 23, 24, 23, 22, 21, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 33, 34, 35, 36, 37, 38, 37, 36, 42, 43, 44, 43, 42, 41, 45, 46, 50, 49, 55, 56, 57, 61, 62, 61, 60, 61, 62, 63, 64, 63, 69, 70, 69, 73, 74, 73, 74, 73, 79, 85, 86, 85, 86, 87]]

Single random walk:

10 full walks:

10 full walks transposed:

250 random walks with “clumsiness”:

500 random walks with “clumsiness”:

---

Python Data Science Toolbox (Part I)

Chapter 1 - Writing your own functions

User-defined functions - with/without parameters, and with/without returning values:

• The general syntax is def myFunction(myParams) : commands # commands can be on additional lines provided that they are indented by 4 spaces
  
• Parameters are defined in the function body, while arguments are passed to the function; so def myFunction(myParams) has a parameter while myFunction(myArg) provides an argument
  
• If the command return myReturn is included in the function, then myReturn will be passed back to the main body of the code and the function will stop and return
  
• Docstrings serve as documentation for a function, and are included in the first line after the function call, surrounded by triple quotes (“”“myComment”“”)

Multiple parameters and return values:

• Functions can accept multiple arguments, such as def myFunction(myParam1, myParam2)
  
• Can also return multiple values using tuples (like a list, but immutable and defined using parentheses rather than square brackets)
  
• Tuples can be unpacked using just variables separated by commans, so for example:
  • myTuple = (2, 4, 6)
    
  • a, b, c = myTuple # a will be 2, b will be 4, c will be 6
    
  • myTuple[1] will be 4, so the tuples can be accessed by way of an index

Bringing it all together - practical examples using Twitter data:

• Function header, function body including a docstring, and (optionally) function returns

Example code includes:

# Define the function shout
def shout():
    """Print a string with three exclamation marks"""
    # Concatenate the strings: shout_word
    shout_word = "congratulations" + "!!!"
    
    # Print shout_word
    print(shout_word)

# Call shout
shout()


# Define shout with the parameter, word
def shout(word):
    """Print a string with three exclamation marks"""
    # Concatenate the strings: shout_word
    shout_word = word + '!!!'

    # Print shout_word
    print(shout_word)

# Call shout with the string 'congratulations'
shout("congratulations")


# Define shout with the parameter, word
def shout(word):
    """Return a string with three exclamation marks"""
    # Concatenate the strings: shout_word
    shout_word = word + "!!!"

    # Replace print with return
    return(shout_word)

# Pass 'congratulations' to shout: yell
yell = shout("congratulations")

# Print yell
print(yell)


# Define shout with parameters word1 and word2
def shout(word1, word2):
    """Concatenate strings with three exclamation marks"""
    # Concatenate word1 with '!!!': shout1
    shout1 = word1 + "!!!"
    
    # Concatenate word2 with '!!!': shout2
    shout2 = word2 + "!!!"
    
    # Concatenate shout1 with shout2: new_shout
    new_shout = shout1 + shout2

    # Return new_shout
    return new_shout

# Pass 'congratulations' and 'you' to shout(): yell
yell = shout("congratulations", "you")

# Print yell
print(yell)


# Set up the nums tuple for later access
nums = (3, 4, 6)

# Unpack nums into num1, num2, and num3
num1, num2, num3 = nums

# Construct even_nums
even_nums = (2, num2, num3)


# Define shout_all with parameters word1 and word2
def shout_all(word1, word2):
    
    # Concatenate word1 with '!!!': shout1
    shout1 = word1 + "!!!"
    
    # Concatenate word2 with '!!!': shout2
    shout2 = word2 + "!!!"
    
    # Construct a tuple with shout1 and shout2: shout_words
    shout_words = (shout1, shout2)

    # Return shout_words
    return shout_words

# Pass 'congratulations' and 'you' to shout_all(): yell1, yell2
yell1, yell2 = shout_all("congratulations", "you")

# Print yell1 and yell2
print(yell1)
print(yell2)


# Import pandas
import pandas as pd

# DO NOT HAVE THIS CSV; CAN JUST MAKE A COLUMN WITH A SINGLE WORD FOR THE EXAMPLE
# Import Twitter data as DataFrame: df
df = pd.read_csv("tweets.csv")

# Initialize an empty dictionary: langs_count
langs_count = {}

# Extract column from DataFrame: col
col = df['lang']

# Iterate over lang column in DataFrame
for entry in col:

    # If the language is in langs_count, add 1
    if entry in langs_count.keys():
        langs_count[entry] = langs_count[entry] + 1
    # Else add the language to langs_count, set the value to 1
    else:
        langs_count[entry] = 1

# Print the populated dictionary
print(langs_count)


# Define count_entries()
def count_entries(df, col_name):
    """Return a dictionary with counts of 
    occurrences as value for each key."""

    # Initialize an empty dictionary: langs_count
    langs_count = {}
    
    # Extract column from DataFrame: col
    col = df[col_name]
    
    # Iterate over lang column in DataFrame
    for entry in col:

        # If the language is in langs_count, add 1
        if entry in langs_count.keys():
            langs_count[entry] = langs_count[entry] + 1
        # Else add the language to langs_count, set the value to 1
        else:
            langs_count[entry] = 1

    # Return the langs_count dictionary
    return(langs_count)

# NEED TO CREATE tweets_df such that it contains a column 'lang'
# Call count_entries(): result
tweets_df = df
result = count_entries(tweets_df, "lang")

# Print the result
print(result)

## congratulations!!!
## congratulations!!!
## congratulations!!!
## congratulations!!!you!!!
## congratulations!!!
## you!!!
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18}
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18}

---

Chapter 2 - Default arguments and variable-length arguments

Scope (where are objects or names accessible) and user-defined functions:

• Global scope - defined in the main body of the script
  
• Local scope - defined inside a function (once the function ends, the names and objects from the function disappear)
  
• Built-in scope - names in the pre-defined built-ins module (e.g., print)
  • To access the builtins, type “import builtins” followed by “dir(builtins)” - long story as to why
    
• Search path for a name/object is local scope, then global scope, then built-in scope
  
• Can use the key word “global” within a function to access the global scope, meaning that variable will be edited in the global scope, not in the local scope
  • def square(value) : global new_val ; new_val = new_val ** 2; return(new_ val)
    
  • new_val = 10 ; square(3) # returns 100, but now the global variable new_val is also 100

Nested functions - one function defined inside another function:

• With nested functions, the search is first local, then to the enclosing function, then to the global scope, then to the builtins
  
• The inner function can be helpful if the outer function will need to repeat certain actions to achieve its objectives
  
• Can return an inner function as the output of a function (example being raising to a user-specified power)
  
• There is a computer-science term “closure” that defines exactly how the scopes work during this process
  • Per DataCamp, “One other pretty cool reason for nesting functions is the idea of a closure. This means that the nested or inner function remembers the state of its enclosing scope when called.”
    
  • Continuing the DataCamp quote “Thus, anything defined locally in the enclosing scope is available to the inner function even when the outer function has finished execution.”
    
• The keyword “nonlocal” is available for changing names/values in the enclosing scope (not the global scope; that is keyword “global”)

Default and flexible arguments - arguments used when they are not specified, or when a flexible number of arguments can be passed:

• The default arguments are defined using the equal sign, same as R (can be over-ridden if passed by the user, otherwise the default value will be used)
  
• Using a parameter *args (anything with a single star) will create a tuple called “args” out of whatever the user-passed (1 or more arguments)
  • It appears from the example that Python has the += command (as well as -=, *= and /=)
    
• Using parameter **kwargs (anything with a double-star) will create a dictionary kwargs with key, value pairs off whatever the user has entered

Bringing it all together - case study on processing a data frame to get word counts, defaulted to column ‘lang’:

• Objective is to further generalize the process to be able to work on any number (arbitrary, user-specified) of columns in the DataFrame

Example code includes:

# Create a string: team
team = "teen titans"

# Define change_team()
def change_team():
    """Change the value of the global variable team."""

    # Use team in global scope
    global team

    # Change the value of team in global: team
    team = "justice league"

# Print team
print(team)

# Call change_team()
change_team()

# Print team
print(team)


# Define three_shouts
def three_shouts(word1, word2, word3):
    """Returns a tuple of strings
    concatenated with '!!!'."""

    # Define inner
    def inner(word):
        """Returns a string concatenated with '!!!'."""
        return word + '!!!'

    # Return a tuple of strings
    return (inner(word1), inner(word2), inner(word3))

# Call three_shouts() and print
print(three_shouts('a', 'b', 'c'))


# Define echo
def echo(n):
    """Return the inner_echo function."""

    # Define inner_echo
    def inner_echo(word1):
        """Concatenate n copies of word1."""
        echo_word = word1 * n
        return echo_word

    # Return inner_echo
    return inner_echo

# Call echo: twice
twice = echo(2)

# Call echo: thrice
thrice = echo(3)

# Call twice() and thrice() then print
print(twice('hello'), thrice('hello'))


# Define echo_shout()
def echo_shout(word):
    """Change the value of a nonlocal variable"""
    
    # Concatenate word with itself: echo_word
    echo_word = word + word
    
    #Print echo_word
    print(echo_word)
    
    # Define inner function shout()
    def shout():
        """Alter a variable in the enclosing scope"""    
        #Use echo_word in nonlocal scope
        nonlocal echo_word
        
        #Change echo_word to echo_word concatenated with '!!!'
        echo_word = echo_word + "!!!"
    
    # Call function shout()
    shout()
    
    #Print echo_word
    print(echo_word)

#Call function echo_shout() with argument 'hello'    
echo_shout("hello")


# Define shout_echo
def shout_echo(word1, echo=1):
    """Concatenate echo copies of word1 and three
     exclamation marks at the end of the string."""

    # Concatenate echo copies of word1 using *: echo_word
    echo_word = word1 * echo

    # Concatenate '!!!' to echo_word: shout_word
    shout_word = echo_word + '!!!'

    # Return shout_word
    return shout_word

# Call shout_echo() with "Hey": no_echo
no_echo = shout_echo("Hey")

# Call shout_echo() with "Hey" and echo=5: with_echo
with_echo = shout_echo("Hey", 5)

# Print no_echo and with_echo
print(no_echo)
print(with_echo)


# Define shout_echo
def shout_echo(word1, echo=1, intense=False):
    """Concatenate echo copies of word1 and three
    exclamation marks at the end of the string."""

    # Concatenate echo copies of word1 using *: echo_word
    echo_word = word1 * echo

    # Capitalize echo_word if intense is True
    if intense is True:
        # Capitalize and concatenate '!!!': echo_word_new
        echo_word_new = echo_word.upper() + '!!!'
    else:
        # Concatenate '!!!' to echo_word: echo_word_new
        echo_word_new = echo_word + '!!!'

    # Return echo_word_new
    return echo_word_new

# Call shout_echo() with "Hey", echo=5 and intense=True: with_big_echo
with_big_echo = shout_echo("Hey", 5, True)

# Call shout_echo() with "Hey" and intense=True: big_no_echo
big_no_echo = shout_echo("Hey", intense=True)

# Print values
print(with_big_echo)
print(big_no_echo)


# Define gibberish
def gibberish(*args):
    """Concatenate strings in *args together."""

    # Initialize an empty string: hodgepodge
    hodgepodge = ""

    # Concatenate the strings in args
    for word in args:
        hodgepodge += word

    # Return hodgepodge
    return(hodgepodge)

# Call gibberish() with one string: one_word
one_word = gibberish("luke")

# Call gibberish() with five strings: many_words
many_words = gibberish("luke", "leia", "han", "obi", "darth")

# Print one_word and many_words
print(one_word)
print(many_words)


# Define report_status
def report_status(**kwargs):
    """Print out the status of a movie character."""

    print("\nBEGIN: REPORT\n")

    # Iterate over the key-value pairs of kwargs
    for key, value in kwargs.items():
        # Print out the keys and values, separated by a colon ':'
        print(key + ": " + value)

    print("\nEND REPORT")

# First call to report_status()
report_status(name="luke", affiliation="jedi", status="missing")

# Second call to report_status()
report_status(name="anakin", affiliation="sith lord", status="deceased")


# DO NOT HAVE file tweets_df (may need to create some dummy data . . . )
import pandas as pd
tweets_df = pd.read_csv("tweets.csv")


# Define count_entries()
def count_entries(df, col_name="lang"):
    """Return a dictionary with counts of
    occurrences as value for each key."""

    # Initialize an empty dictionary: cols_count
    cols_count = {}

    # Extract column from DataFrame: col
    col = df[col_name]
    
    # Iterate over the column in DataFrame
    for entry in col:

        # If entry is in cols_count, add 1
        if entry in cols_count.keys():
            cols_count[entry] += 1

        # Else add the entry to cols_count, set the value to 1
        else:
            cols_count[entry] = 1

    # Return the cols_count dictionary
    return cols_count

# Call count_entries(): result1
result1 = count_entries(tweets_df)

# Call count_entries(): result2
result2 = count_entries(tweets_df, "source")

# Print result1 and result2
print(result1)
print(result2)


# Define count_entries()
def count_entries(df, *args):
    """Return a dictionary with counts of
    occurrences as value for each key."""
    
    #Initialize an empty dictionary: cols_count
    cols_count = {}
    
    # Iterate over column names in args
    for col_name in args:
    
        # Extract column from DataFrame: col
        col = df[col_name]
    
        # Iterate over the column in DataFrame
        for entry in col:
    
            # If entry is in cols_count, add 1
            if entry in cols_count.keys():
                cols_count[entry] += 1
    
            # Else add the entry to cols_count, set the value to 1
            else:
                cols_count[entry] = 1

    # Return the cols_count dictionary
    return cols_count

# Call count_entries(): result1
result1 = count_entries(tweets_df, "lang")

# Call count_entries(): result2
result2 = count_entries(tweets_df, "lang", "source")

# Print result1 and result2
print(result1)
print(result2)

## teen titans
## justice league
## ('a!!!', 'b!!!', 'c!!!')
## hellohello hellohellohello
## hellohello
## hellohello!!!
## Hey!!!
## HeyHeyHeyHeyHey!!!
## HEYHEYHEYHEYHEY!!!
## HEY!!!
## luke
## lukeleiahanobidarth
## 
## BEGIN: REPORT
## 
## name: luke
## affiliation: jedi
## status: missing
## 
## END REPORT
## 
## BEGIN: REPORT
## 
## name: anakin
## affiliation: sith lord
## status: deceased
## 
## END REPORT
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18}
## {'C': 60, 'A': 57, 'D': 35, 'B': 48}
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18}
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18, 'C': 60, 'A': 57, 'D': 35, 'B': 48}

---

Chapter 3 - Lambda functions and error handling

Lambda functions - quicker way to write functions on the fly:

• The general syntax is lambda : expression
  • raise_to_power = lambda x, y : x ** y
    
  • raise_to_power(2, 3) = 8 # runs as 2 ** 3
    
• The lambda function is “quick and dirty”, so it should be limited to key areas where that is appropriate
  
• An example is map(func, seq) which will apply the function over all elements of the sequence
  • The lambda function can be valuable here, since it allows for a custom function to be applied quickly across a sequence
    
  • square_all = map(lambda num: num ** 2, nums)
    
  • Need to use print(list(square_all)) since print(square_all) will just define that it is an object at a designated point in memory

Introduction to error handling - functions generally return an error if something is wrong, though that can be trapped/over-ridden:

• Endeavor to provide useful error messages rather than just a trace-back default from Python
  
• The typical approach in Python is try-except, where try will try the command and except will run if the try produces an error
  • try : command to try # The commands to try are typically on a new line(s) and indented by 4 spaces
    
  • except : do otherwise # The except lines up with the try, while the do otherwise are typically on a new line(s) and indented by 4 spaces
    
• Can add types of errors to be trapped, for example “type errors only” using except TypeError :
  
• To generate an error (for example, if negative inputs are not desired), can use raise ValueError(“quotedMessage”)
  • This will throw a trace-back message, with the quotedMessage appearing at the bottom

Bringing it all together:

• Case study for error handling on the tweets data frame

Example code includes:

# Define echo_word as a lambda function: echo_word
echo_word = (lambda word1, echo : word1 * echo)

# Call echo_word: result
result = echo_word("hey", 5)

# Print result
print(result)


# Create a list of strings: spells
spells = ["protego", "accio", "expecto patronum", "legilimens"]

# Use map() to apply a lambda function over spells: shout_spells
shout_spells = map(lambda a : a + "!!!", spells)

# Convert shout_spells to a list: shout_spells_list
shout_spells_list = list(shout_spells)

# Convert shout_spells into a list and print it
print(shout_spells_list)


# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']

# Use filter() to apply a lambda function over fellowship: result
result = filter(lambda a : len(a) > 6, fellowship)

# Convert result to a list: result_list
result_list = list(result)

# Convert result into a list and print it
print(result_list)


# Import reduce from functools
from functools import reduce

# Create a list of strings: stark
stark = ['robb', 'sansa', 'arya', 'eddard', 'jon']

# Use reduce() to apply a lambda function over stark: result
result = reduce(lambda item1, item2 : item1 + item2, stark)

# Print the result
print(result)


# Define shout_echo
def shout_echo(word1, echo=1):
    """Concatenate echo copies of word1 and three
    exclamation marks at the end of the string."""

    # Initialize empty strings: echo_word, shout_words
    echo_word = ""
    shout_words = ""

    # Add exception handling with try-except
    try:
        # Concatenate echo copies of word1 using *: echo_word
        echo_word = word1 * echo

        # Concatenate '!!!' to echo_word: shout_words
        shout_words = echo_word + "!!!"
    except:
        # Print error message
        print("word1 must be a string and echo must be an integer.")

    # Return shout_words
    return shout_words

# Call shout_echo
shout_echo("particle", echo="accelerator")


# Define shout_echo
def shout_echo(word1, echo=1):
    """Concatenate echo copies of word1 and three
    exclamation marks at the end of the string."""

    # Raise an error with raise
    if echo < 0:
        raise ValueError('echo must be greater than 0')

    # Concatenate echo copies of word1 using *: echo_word
    echo_word = word1 * echo

    # Concatenate '!!!' to echo_word: shout_word
    shout_word = echo_word + '!!!'

    # Return shout_word
    return shout_word

# Call shout_echo
shout_echo("particle", echo=5)


# DO NOT HAVE file tweets_df (made "tweets.csv" using R)
import pandas as pd
tweets_df = pd.read_csv("tweets.csv")

# Select retweets from the Twitter DataFrame: result
result = filter(lambda x : x[0:2] == "RT", tweets_df["text"])

# Create list from filter object result: res_list
res_list = list(result)

# Print all retweets in res_list
for tweet in res_list:
    print(tweet)


# Define count_entries()
def count_entries(df, col_name='lang'):
    """Return a dictionary with counts of
    occurrences as value for each key."""

    # Initialize an empty dictionary: cols_count
    cols_count = {}

    # Add try block
    try:
        # Extract column from DataFrame: col
        col = df[col_name]
        
        # Iterate over the column in dataframe
        for entry in col:
    
            # If entry is in cols_count, add 1
            if entry in cols_count.keys():
                cols_count[entry] += 1
            # Else add the entry to cols_count, set the value to 1
            else:
                cols_count[entry] = 1
    
        # Return the cols_count dictionary
        return cols_count

    # Add except block
    except:
        print('The DataFrame does not have a ' + col_name + ' column.')

# DO NOT HAVE file tweets_df
# Call count_entries(): result1
result1 = count_entries(tweets_df, 'lang')

# Print result1
print(result1)

# Call count_entries(): result2
result2 = count_entries(tweets_df, 'lang1')


# Define count_entries()
def count_entries(df, col_name='lang'):
    """Return a dictionary with counts of
    occurrences as value for each key."""
    
    # Raise a ValueError if col_name is NOT in DataFrame
    if col_name not in df.columns:
        raise ValueError('The DataFrame does not have a ' + col_name + ' column.')

    # Initialize an empty dictionary: cols_count
    cols_count = {}
    
    # Extract column from DataFrame: col
    col = df[col_name]
    
    # Iterate over the column in DataFrame
    for entry in col:

        # If entry is in cols_count, add 1
        if entry in cols_count.keys():
            cols_count[entry] += 1
            # Else add the entry to cols_count, set the value to 1
        else:
            cols_count[entry] = 1
        
        # Return the cols_count dictionary
    return cols_count

# Call count_entries(): result1
result1 = count_entries(tweets_df, "lang")

# Print result1
print(result1)


# CAREFUL, THIS ONE IS DESIGNED TO RAISE THE ERROR!
# count_entries(tweets_df, 'lang1')

## heyheyheyheyhey
## ['protego!!!', 'accio!!!', 'expecto patronum!!!', 'legilimens!!!']
## ['samwise', 'aragorn', 'legolas', 'boromir']
## robbsansaaryaeddardjon
## word1 must be a string and echo must be an integer.
## RT H
## RT F
## RT H
## RT H
## RT G
## RT G
## RT G
## RT E
## RT E
## RT E
## RT H
## RT E
## RT F
## RT G
## RT G
## RT E
## RT G
## RT E
## RT E
## RT H
## RT G
## RT E
## RT G
## RT G
## RT F
## RT H
## RT H
## RT E
## RT H
## RT G
## RT F
## RT F
## RT F
## RT H
## RT G
## RT E
## RT E
## RT H
## RT F
## RT G
## RT H
## RT E
## RT H
## RT G
## RT F
## RT E
## RT F
## RT E
## RT E
## RT H
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18}
## The DataFrame does not have a lang1 column.
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18}

Python Data Science Toolbox (Part II)

Chapter 1 - Using iterators in PythonLand

Introduction to iterators - for loops and the like:

• For loops can be used for iterating over strings, lists, dictionaries, range() objects, and the like
  
• Anything that can be looped over is called an “iterable”, and will have an associated iter() method
  
• An “iterator” is something that produces the next value with a next() call
  
• For loops are using the “iterable” property of objects under-the-hood, with an associated (if silent) “next” call
  • word = “Da”; it = iter(word); next(it); next(it) # “D” then “a”
    
• The “star” (*) operator will impact all elements of an iterator at once
  • word = “Data”; it = iter(word); print(*it) # single-line of “D” “a” “t” “a”
    
  • Note that if print(*it) were then called again, there would just be a blank line; there is nothing left to iterate over
    
• To unpack a dictionary, use myDict.items()
  
• To iterate over a file, use file=open(“file.txt”); it = iter(file); print(next(it))

Playing with iterators - enumerate and zip:

• The function enumerate(myIterable) returns an “enumerate” class object with both items and their indices
  • Running list() on the “enumerate” class object will make a list out of the tuples [(index1, item1), (index2, item2), . . . ]
    
  • The default is for index1=0, though the argument start= may be included in the enumerate() call for a different starting index
    
• The zip(myIter01, myIter02) will create a “zip” class object of the iterators
  • list(zip()) will return a list of tuples, starting with (allItem1), (allIterm2), . . .
  • Seems to require that the iterators all be the same length, else only items until running out of the shortest iterator will be tupled

Using iterators to load large files in to memory - loading data in chunks:

• Common strategy with large files is to read in some data, process it, save the results, discard the data, and then repeat
  
• The pandas.read_csv() has an option for chunksize= that allows for reading chunks of any given size

Example code includes:

# Create a list of strings: flash
flash = ['jay garrick', 'barry allen', 'wally west', 'bart allen']

# Print each list item in flash using a for loop
for person in flash : print(person)

# Create an iterator for flash: superspeed
superspeed = iter(flash)

# Print each item from the iterator
print(next(superspeed))
print(next(superspeed))
print(next(superspeed))
print(next(superspeed))


# Create an iterator for range(3): small_value
small_value = iter(range(3))

# Print the values in small_value
print(next(small_value))
print(next(small_value))
print(next(small_value))

# Loop over range(3) and print the values
for num in range(3) : print(num)


# Create an iterator for range(10 ** 100): googol
googol = iter(range(10 ** 100))

# Print the first 5 values from googol
print(next(googol))
print(next(googol))
print(next(googol))
print(next(googol))
print(next(googol))


# Create a range object: values
values = range(10, 21)

# Print the range object
print(values)

# Create a list of integers: values_list
values_list = list(values)

# Print values_list
print(values_list)

# Get the sum of values: values_sum
values_sum = sum(values)

# Print values_sum
print(values_sum)


# Create a list of strings: mutants
mutants = ['charles xavier', 
            'bobby drake', 
            'kurt wagner', 
            'max eisenhardt', 
            'kitty pride']

# Create a list of tuples: mutant_list
mutant_list = list(enumerate(mutants))

# Print the list of tuples
print(mutant_list)

# Unpack and print the tuple pairs
for index1, value1 in mutant_list :
    print(index1, value1)

# Change the start index
for index2, value2 in list(enumerate(mutants, start=1)) :
    print(index2, value2)


aliases = ['prof x', 'iceman', 'nightcrawler', 'magneto', 'shadowcat']
powers = ['telepathy', 'thermokinesis', 'teleportation', 'magnetokinesis', 'intangibility' ]

# Create a list of tuples: mutant_data
mutant_data = list(zip(mutants, aliases, powers))

# Print the list of tuples
print(mutant_data)

# Create a zip object using the three lists: mutant_zip
mutant_zip = zip(mutants, aliases, powers)

# Print the zip object
print(mutant_zip)

# Unpack the zip object and print the tuple values
for value1, value2, value3 in mutant_zip :
    print(value1, value2, value3)


# Create a zip object from mutants and powers: z1
z1 = zip(mutants, powers)

# Print the tuples in z1 by unpacking with *
print(*z1)

# Re-create a zip object from mutants and powers: z1
z1 = zip(mutants, powers)

# 'Unzip' the tuples in z1 by unpacking with * and zip(): result1, result2
result1, result2 = zip(*z1)

# Check if unpacked tuples are equivalent to original tuples
print(result1 == tuple(mutants))
print(result2 == tuple(powers))


import pandas as pd

# Initialize an empty dictionary: counts_dict
counts_dict = dict()

# DO NOT HAVE FILE tweets.csv
# Created in R - see above for code
# Iterate over the file chunk by chunk
for chunk in pd.read_csv("tweets.csv", chunksize=10):
    # Iterate over the column in DataFrame
    for entry in chunk['lang']:
        if entry in counts_dict.keys():
            counts_dict[entry] += 1
        else:
            counts_dict[entry] = 1

# Print the populated dictionary
print(counts_dict)


# Define count_entries()
def count_entries(csv_file, c_size, colname):
    """Return a dictionary with counts of
    occurrences as value for each key."""
    
    # Initialize an empty dictionary: counts_dict
    counts_dict = {}

    # Iterate over the file chunk by chunk
    for chunk in pd.read_csv(csv_file, chunksize=c_size):

        # Iterate over the column in DataFrame
        for entry in chunk[colname]:
            if entry in counts_dict.keys():
                counts_dict[entry] += 1
            else:
                counts_dict[entry] = 1

    # Return counts_dict
    return counts_dict

# Call count_entries(): result_counts
result_counts = count_entries("tweets.csv", 10, "lang")

# Print result_counts
print(result_counts)

## jay garrick
## barry allen
## wally west
## bart allen
## jay garrick
## barry allen
## wally west
## bart allen
## 0
## 1
## 2
## 0
## 1
## 2
## 0
## 1
## 2
## 3
## 4
## range(10, 21)
## [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
## 165
## [(0, 'charles xavier'), (1, 'bobby drake'), (2, 'kurt wagner'), (3, 'max eisenhardt'), (4, 'kitty pride')]
## 0 charles xavier
## 1 bobby drake
## 2 kurt wagner
## 3 max eisenhardt
## 4 kitty pride
## 1 charles xavier
## 2 bobby drake
## 3 kurt wagner
## 4 max eisenhardt
## 5 kitty pride
## [('charles xavier', 'prof x', 'telepathy'), ('bobby drake', 'iceman', 'thermokinesis'), ('kurt wagner', 'nightcrawler', 'teleportation'), ('max eisenhardt', 'magneto', 'magnetokinesis'), ('kitty pride', 'shadowcat', 'intangibility')]
## <zip object at 0x00672030>
## charles xavier prof x telepathy
## bobby drake iceman thermokinesis
## kurt wagner nightcrawler teleportation
## max eisenhardt magneto magnetokinesis
## kitty pride shadowcat intangibility
## ('charles xavier', 'telepathy') ('bobby drake', 'thermokinesis') ('kurt wagner', 'teleportation') ('max eisenhardt', 'magnetokinesis') ('kitty pride', 'intangibility')
## True
## True
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18}
## {'en': 159, 'fr': 10, 'it': 13, 'sp': 18}

---

Chapter 2 - List comprehensions and generators

List comprehensions help address some of the inefficiencies (coding, run time, etc.) of using for loops for some tasks:

• The syntax is [myDesiredCalcs for myVar in myIter] # should be square-bracketed
  
• List comprehension may be used over any iterable; for example, [num for num in range(11)] will return [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
  
• List comprehension can also be valuable in lieu of nested for loops; for example, with [(num1, num2) for num1 in range(0, 2) for num2 in range(6, 8)]
  
• There is sometimes a trade-off for readability to keep in mind

Advanced comprehensions - additional functionality available:

• [myDesiredCalcs for myVar in myIter if myCond] # allows for myCond to limit the myVar that are available to myDesiredCalcs (thus limiting the output list)
  
• [myDesiredCalcs if myCond else myDefault for myVar in myIter] # allows for extracting where myCond is met and replacing with myDefault otherwise
  
• {myCalc01 : myCalc02 for myVar in myIter} # produces a dictionary with key myCalc01 and value myCalc02

Introduction to generator expressions - creating generator objects rather than list/dictionaries:

• (myDesiredCalcs for myVar in myIter) # will create a generator object rather than a list
  
• Using a generator expression can help significantly with large sequences due to “lazy evaluation” (not evaluated until needed, such as next() being called)
  • While [num for num in range(10 ** 1000000)] will bomb out of memory, (num for num in range(10 ** 1000000)) is OK!
    
• An additional nice feature is that all the conditionals can be run in the generator expression also
  
• Note that using “yield” rather than “return” in a def (function) will build a generator function (it will return a generator object when called)

Wrapping up comprehensions and generators - helps with wrangling data:

• Basic form - enclosed in brackets, output will be a list
  
• More advanced forms - conditions on the iterator and/or iterable
  
• Dictionaries - enclosed in braces
  
• Generators - enclosed in parentheses

Example code includes:

doctor = ['house', 'cuddy', 'chase', 'thirteen', 'wilson']
[doc[0] for doc in doctor]

# Create list comprehension: squares
squares = [i ** 2 for i in range(0, 10)]


# Create a 5 x 5 matrix using a list of lists: matrix
matrix = [[col for col in range(5)] for row in range(5)]

# Print the matrix
for row in matrix:
    print(row)


# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']

# Create list comprehension: new_fellowship
new_fellowship = [member for member in fellowship if len(member) >= 7]

# Print the new list
print(new_fellowship)


# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']

# Create list comprehension: new_fellowship
new_fellowship = [member if len(member) >= 7 else "" for member in fellowship]

# Print the new list
print(new_fellowship)


# Create a list of strings: fellowship
fellowship = ['frodo', 'samwise', 'merry', 'aragorn', 'legolas', 'boromir', 'gimli']

# Create dict comprehension: new_fellowship
new_fellowship = {member : len(member) for member in fellowship}

# Print the new list
print(new_fellowship)


# Create generator object: result
result = (num for num in range(16))

# Print the first 5 values
print(next(result))
print(next(result))
print(next(result))
print(next(result))
print(next(result))

# Print the rest of the values
# NOTE - only will print 5-15 since 0-4 have previously been "consumed" above
for value in result:
    print(value)


# Create a list of strings: lannister
lannister = ['cersei', 'jaime', 'tywin', 'tyrion', 'joffrey']

# Create a generator object: lengths
lengths = (len(person) for person in lannister)

# Iterate over and print the values in lengths
for value in lengths:
    print(value)


# Create a list of strings
lannister = ['cersei', 'jaime', 'tywin', 'tyrion', 'joffrey']

# Define generator function get_lengths
def get_lengths(input_list):
    """Generator function that yields the
    length of the strings in input_list."""
    # Yield the length of a string
    for person in input_list:
        yield len(person)

# Print the values generated by get_lengths()
for value in get_lengths(lannister):
    print(value)


# DO NOT HAVE panda "df"
# Extract the created_at column from df: tweet_time
# tweet_time = df["created_at"]

# Extract the clock time: tweet_clock_time
# tweet_clock_time = [entry[11:19] for entry in tweet_time]

# Print the extracted times
# print(tweet_clock_time)


# Extract the created_at column from df: tweet_time
# tweet_time = df['created_at']

# Extract the clock time: tweet_clock_time
# tweet_clock_time = [entry[11:19] for entry in tweet_time if entry[17:19] == "19"]

# Print the extracted times
# print(tweet_clock_time)

## [0, 1, 2, 3, 4]
## [0, 1, 2, 3, 4]
## [0, 1, 2, 3, 4]
## [0, 1, 2, 3, 4]
## [0, 1, 2, 3, 4]
## ['samwise', 'aragorn', 'legolas', 'boromir']
## ['', 'samwise', '', 'aragorn', 'legolas', 'boromir', '']
## {'frodo': 5, 'samwise': 7, 'merry': 5, 'aragorn': 7, 'legolas': 7, 'boromir': 7, 'gimli': 5}
## 0
## 1
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 6
## 5
## 5
## 6
## 7
## 6
## 5
## 5
## 6
## 7

---

Chapter 3 - Bringing it all together (case study)

Welcome to the case study - previous two course techniques:

• Wrangle and extract data from the World Bank Indicators dataset (1960-2015 data on 227 countries)
  
• Recall that zip(a, b, . . . ) creates an iterable of tuples conmtaining (a1, b1, …), (a2, b2, …), …

Using Python generators for streaming data:

• Generators are helpful for reading large files - in fact, they work even on files that are being written (as long as the read stays behind the write)
  
• Generator functions are written like regular functions, but they have a “yield” (put it in to the generator and keep going) rather than a “return” (return the value and stop)
  
• Goal will be to write a generator to read streaming data

Reading files in chunks with pandas.read_csv():

• pandas.read_csv(file, chunksize= ) # allows the file to be read in chunks of size chunksize

Example code includes:

row_vals = [ 'Arab World', 'ARB', 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'SP.ADO.TFRT', '1960', '133.56090740552298' ]

feature_names = [ 'CountryName', 'CountryCode', 'IndicatorName', 'IndicatorCode', 'Year', 'Value' ]

# Zip lists: zipped_lists
zipped_lists = zip(feature_names, row_vals)

# Create a dictionary: rs_dict
rs_dict = dict(zipped_lists)

# Print the dictionary
print(rs_dict)


# Define lists2dict()
def lists2dict(list1, list2):
    """Return a dictionary where list1 provides
    the keys and list2 provides the values."""
    
    # Zip lists: zipped_lists
    zipped_lists = zip(list1, list2)
    
    # Create a dictionary: rs_dict
    rs_dict = dict(zipped_lists)
    
    # Return the dictionary
    return rs_dict

# Call lists2dict: rs_fxn
rs_fxn = lists2dict(feature_names, row_vals)

# Print rs_fxn
print(rs_fxn)


# Create list row_lists
regn = ['Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World', 'Arab World']

abb = ['ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB', 'ARB']

indName = ['Adolescent fertility rate (births per 1,000 women ages 15-19)', 'Age dependency ratio (% of working-age population)', 'Age dependency ratio, old (% of working-age population)', 'Age dependency ratio, young (% of working-age population)', 'Arms exports (SIPRI trend indicator values)', 'Arms imports (SIPRI trend indicator values)', 'Birth rate, crude (per 1,000 people)', 'CO2 emissions (kt)', 'CO2 emissions (metric tons per capita)', 'CO2 emissions from gaseous fuel consumption (% of total)', 'CO2 emissions from liquid fuel consumption (% of total)', 'CO2 emissions from liquid fuel consumption (kt)', 'CO2 emissions from solid fuel consumption (% of total)', 'Death rate, crude (per 1,000 people)', 'Fertility rate, total (births per woman)', 'Fixed telephone subscriptions', 'Fixed telephone subscriptions (per 100 people)', 'Hospital beds (per 1,000 people)', 'International migrant stock (% of population)', 'International migrant stock, total' ]

indCode = ['SP.ADO.TFRT', 'SP.POP.DPND', 'SP.POP.DPND.OL', 'SP.POP.DPND.YG', 'MS.MIL.XPRT.KD', 'MS.MIL.MPRT.KD', 'SP.DYN.CBRT.IN', 'EN.ATM.CO2E.KT', 'EN.ATM.CO2E.PC', 'EN.ATM.CO2E.GF.ZS', 'EN.ATM.CO2E.LF.ZS', 'EN.ATM.CO2E.LF.KT', 'EN.ATM.CO2E.SF.ZS', 'SP.DYN.CDRT.IN', 'SP.DYN.TFRT.IN', 'IT.MLT.MAIN', 'IT.MLT.MAIN.P2', 'SH.MED.BEDS.ZS', 'SM.POP.TOTL.ZS', 'SM.POP.TOTL']

year = ['1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960', '1960']

value = ['133.56090740552298', '87.7976011532547', '6.634579191565161', '81.02332950839141', '3000000.0', '538000000.0', '47.697888095096395', '59563.9892169935', '0.6439635478877049', '5.041291753975099', '84.8514729446567', '49541.707291032304', '4.72698138789597', '19.7544519237187', '6.92402738655897', '406833.0', '0.6167005703199', '1.9296220724398703', '2.9906371279862403', '3324685.0']

row_lists=list(zip(regn, abb, indName, indCode, year, value))

# Print the first two lists in row_lists
print(row_lists[0])
print(row_lists[1])

# Turn list of lists into list of dicts: list_of_dicts
list_of_dicts = [lists2dict(feature_names, sublist) for sublist in row_lists]

# Print the first two dictionaries in list_of_dicts
print(list_of_dicts[0])
print(list_of_dicts[1])

# Import the pandas package
import pandas as pd

# Turn list of lists into list of dicts: list_of_dicts
list_of_dicts = [lists2dict(feature_names, sublist) for sublist in row_lists]

# Turn list of dicts into a DataFrame: df
df = pd.DataFrame(list_of_dicts)

# Print the head of the DataFrame
print(df.head())

# REFERENCE DATA POSSIBLY AT http://data.worldbank.org/data-catalog/world-development-indicators
# Created relevant file "world_dev_ind.csv" using Python and World Bank download
# Open a connection to the file
with open("world_dev_ind.csv") as file:
    
    # Skip the column names
    file.readline()
    
    # Initialize an empty dictionary: counts_dict
    counts_dict = {}
    
    # Process only the first 1000 rows
    for j in range(1000):
        
        # Split the current line into a list: line
        line = file.readline().split(',')
        
        # Get the value for the first column: first_col
        first_col = line[0]
        
        # If the column value is in the dict, increment its value
        if first_col in counts_dict.keys():
            counts_dict[first_col] += 1
        
        # Else, add to the dict and set value to 1
        else:
            counts_dict[first_col] = 1

# Print the resulting dictionary
print(counts_dict)

# Define read_large_file()
def read_large_file(file_object):
    """A generator function to read a large file lazily."""
    
    # Loop indefinitely until the end of the file
    while True:
        
        # Read a line from the file: data
        data = file_object.readline()
        
        # Break if this is the end of the file
        if not data:
            break
        
        # Yield the line of data
        yield data
        
# Open a connection to the file
with open('world_dev_ind.csv') as file:
    
    # Create a generator object for the file: gen_file
    gen_file = read_large_file(file)
    
    # Print the first three lines of the file
    print(next(gen_file))
    print(next(gen_file))
    print(next(gen_file))


# Initialize an empty dictionary: counts_dict
counts_dict = {}


# Open a connection to the file
with open("world_dev_ind.csv") as file:
    
    # Iterate over the generator from read_large_file()
    for line in read_large_file(file):
        row = line.split(',')
        first_col = row[0]
        
        if first_col in counts_dict.keys():
            counts_dict[first_col] += 1
        else:
            counts_dict[first_col] = 1

# Print            
print(counts_dict)


# DO NOT HAVE FILE ind_pop.csv (CountryName,CountryCode,IndicatorName,IndicatorCode,Year,Value\n)
# Value for regions of CountryName/CountryCode - fixing Urban population (% of total), SP.URB.TOTL.IN.ZS , 1960
# Just changed it to use "world_dev_ind.csv"
# Import the pandas package
import pandas as pd
import matplotlib.pyplot as plt


# Initialize reader object: df_reader
df_reader = pd.read_csv("world_dev_ind.csv", chunksize=10)

# Print two chunks
print(next(df_reader))
print(next(df_reader))


# DO NOT HAVE FILE ind_pop_data.csv 
# ('CountryName,CountryCode,Year,Total Population,Urban population (% of total)\n)
# Appears to be 1960-1964
# Initialize reader object: urb_pop_reader
# Create file using Python, needs to read in using encoding="latin-1"
urb_pop_reader = pd.read_csv("ind_pop_data.csv", chunksize=2500, encoding="latin-1")

# Get the first DataFrame chunk: df_urb_pop
df_urb_pop = next(urb_pop_reader)

# Check out the head of the DataFrame
print(df_urb_pop.head())

# Check out specific country: df_pop_ceb
idxCeb = df_urb_pop[df_urb_pop["CountryCode"] == "CEB"].index
df_pop_ceb = df_urb_pop.loc[idxCeb, :]  # Make sure it is not just a reference . . . 

# Zip DataFrame columns of interest: pops
pops = zip(df_pop_ceb["Total Population"], df_pop_ceb["Urban population (% of total)"])

# Turn zip object into list: pops_list
pops_list = list(pops)

# Print pops_list
print(pops_list)


# Initialize reader object: urb_pop_reader
urb_pop_reader = pd.read_csv("ind_pop_data.csv", chunksize=2500, encoding="latin-1")

# Get the first DataFrame chunk: df_urb_pop
df_urb_pop = next(urb_pop_reader)

# Check out specific country: df_pop_ceb
idxCeb = df_urb_pop[df_urb_pop["CountryCode"] == "CEB"].index
df_pop_ceb = df_urb_pop.loc[idxCeb, :]  # Make sure it is not just a reference . . . 
# df_pop_ceb = df_urb_pop[df_urb_pop['CountryCode'] == 'CEB']

# Zip DataFrame columns of interest: pops
pops = zip(df_pop_ceb['Total Population'], 
            df_pop_ceb['Urban population (% of total)'])

# Turn zip object into list: pops_list
pops_list = list(pops)

# Use list comprehension to create new DataFrame column 'Total Urban Population'
# df_pop_ceb["Total Urban Population"] = df_pop_ceb["Total Population"]
# a = [int(0.01 * tup[0] * tup[1]) for tup in pops_list]
df_pop_ceb['Total Urban Population'] = [int(0.01 * tup[0] * tup[1]) for tup in pops_list]


# Plot urban population data
df_pop_ceb.plot(kind="scatter", x="Year", y="Total Urban Population")
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy020.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting

# Initialize reader object: urb_pop_reader
urb_pop_reader = pd.read_csv('ind_pop_data.csv', chunksize=1000, encoding="latin-1")

# Initialize empty DataFrame: data
data = pd.DataFrame()

# Iterate over each DataFrame chunk
for df_urb_pop in urb_pop_reader:

    # Check out specific country: df_pop_ceb
    idxCeb = df_urb_pop[df_urb_pop["CountryCode"] == "CEB"].index
    df_pop_ceb = df_urb_pop.loc[idxCeb, :]  # Make sure it is not just a reference . . . 

    # Zip DataFrame columns of interest: pops
    pops = zip(df_pop_ceb['Total Population'],
                df_pop_ceb['Urban population (% of total)'])

    # Turn zip object into list: pops_list
    pops_list = list(pops)

    # Use list comprehension to create new DataFrame column 'Total Urban Population'
    # df_pop_ceb["Total Urban Population"] = df_pop_ceb["Total Population"]
    # a = [int(0.01 * tup[0] * tup[1]) for tup in pops_list]
    df_pop_ceb['Total Urban Population'] = [int(0.01 * tup[0] * tup[1]) for tup in pops_list]
    
    # Append DataFrame chunk to data: data
    data = data.append(df_pop_ceb)

# Plot urban population data
data.plot(kind='scatter', x='Year', y='Total Urban Population')
# plt.show()
# Save as dummy PNG instead
plt.savefig("_dummyPy021.png", bbox_inches="tight")
plt.clf()  # Required to prevent continued over-plotting


# Define plot_pop()
def plot_pop(filename, country_code, pngCode=False):
    
    # Initialize reader object: urb_pop_reader
    urb_pop_reader = pd.read_csv(filename, chunksize=1000, encoding="latin-1")
    
    # Initialize empty DataFrame: data
    data = pd.DataFrame()
    
    # Iterate over each DataFrame chunk
    for df_urb_pop in urb_pop_reader:
        # Check out specific country: df_pop_ceb
        idxCeb = df_urb_pop[df_urb_pop["CountryCode"] == country_code].index
        df_pop_ceb = df_urb_pop.loc[idxCeb, :]  # Make sure it is not just a reference . . . 
        
        # Zip DataFrame columns of interest: pops
        pops = zip(df_pop_ceb['Total Population'],
                    df_pop_ceb['Urban population (% of total)'])
        
        # Turn zip object into list: pops_list
        pops_list = list(pops)
        
        # Use list comprehension to create new DataFrame column 'Total Urban Population'
        # df_pop_ceb["Total Urban Population"] = df_pop_ceb["Total Population"]
        # a = [int(0.01 * tup[0] * tup[1]) for tup in pops_list]
        # df_pop_ceb.loc[df_pop_ceb.index, 'Total Urban Population'] = a
        df_pop_ceb['Total Urban Population'] = [int(0.01 * tup[0] * tup[1]) for tup in pops_list]
        
        # Append DataFrame chunk to data: data
        data = data.append(df_pop_ceb)
        
    # Plot urban population data
    data.plot(kind='scatter', x='Year', y='Total Urban Population')
    if pngCode == False :
        plt.show()  # Plot by default
    else :
        plt.savefig(pngCode, bbox_inches="tight") # Save as dummy PNG instead
    
    plt.clf()  # Required to prevent continued over-plotting

# Set the filename: fn
fn = 'ind_pop_data.csv'

# Call plot_pop for country code 'CEB'
plot_pop(fn, "CEB", "_dummyPy022.png")

# Call plot_pop for country code 'ARB'
plot_pop(fn, "ARB", "_dummyPy023.png")

## {'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}
## {'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}
## ('Arab World', 'ARB', 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'SP.ADO.TFRT', '1960', '133.56090740552298')
## ('Arab World', 'ARB', 'Age dependency ratio (% of working-age population)', 'SP.POP.DPND', '1960', '87.7976011532547')
## {'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Adolescent fertility rate (births per 1,000 women ages 15-19)', 'IndicatorCode': 'SP.ADO.TFRT', 'Year': '1960', 'Value': '133.56090740552298'}
## {'CountryName': 'Arab World', 'CountryCode': 'ARB', 'IndicatorName': 'Age dependency ratio (% of working-age population)', 'IndicatorCode': 'SP.POP.DPND', 'Year': '1960', 'Value': '87.7976011532547'}
##   CountryCode CountryName   IndicatorCode  \
## 0         ARB  Arab World     SP.ADO.TFRT   
## 1         ARB  Arab World     SP.POP.DPND   
## 2         ARB  Arab World  SP.POP.DPND.OL   
## 3         ARB  Arab World  SP.POP.DPND.YG   
## 4         ARB  Arab World  MS.MIL.XPRT.KD   
## 
##                                        IndicatorName               Value  Year  
## 0  Adolescent fertility rate (births per 1,000 wo...  133.56090740552298  1960  
## 1  Age dependency ratio (% of working-age populat...    87.7976011532547  1960  
## 2  Age dependency ratio, old (% of working-age po...   6.634579191565161  1960  
## 3  Age dependency ratio, young (% of working-age ...   81.02332950839141  1960  
## 4        Arms exports (SIPRI trend indicator values)           3000000.0  1960  
## {'Arab World': 6, 'Caribbean small states': 6, 'Central Europe and the Baltics': 6, 'Early-demographic dividend': 6, 'East Asia & Pacific': 6, 'East Asia & Pacific (excluding high income)': 6, 'East Asia & Pacific (IDA & IBRD countries)': 6, 'Euro area': 6, 'Europe & Central Asia': 6, 'Europe & Central Asia (excluding high income)': 6, 'Europe & Central Asia (IDA & IBRD countries)': 6, 'European Union': 6, 'Fragile and conflict affected situations': 6, 'Heavily indebted poor countries (HIPC)': 6, 'High income': 6, 'IBRD only': 6, 'IDA & IBRD total': 6, 'IDA blend': 6, 'IDA only': 6, 'IDA total': 6, 'Late-demographic dividend': 6, 'Latin America & Caribbean': 6, 'Latin America & Caribbean (excluding high income)': 6, 'Latin America & the Caribbean (IDA & IBRD countries)': 6, 'Least developed countries: UN classification': 6, 'Low & middle income': 6, 'Low income': 6, 'Lower middle income': 6, 'Middle East & North Africa': 6, 'Middle East & North Africa (excluding high income)': 6, 'Middle East & North Africa (IDA & IBRD countries)': 6, 'Middle income': 6, 'North America': 6, 'OECD members': 6, 'Other small states': 6, 'Pacific island small states': 6, 'Post-demographic dividend': 6, 'Pre-demographic dividend': 6, 'Small states': 6, 'South Asia': 6, 'South Asia (IDA & IBRD)': 6, 'Sub-Saharan Africa': 6, 'Sub-Saharan Africa (excluding high income)': 6, 'Sub-Saharan Africa (IDA & IBRD countries)': 6, 'Upper middle income': 6, 'World': 6, 'Afghanistan': 6, 'Albania': 6, 'Algeria': 6, 'American Samoa': 6, 'Andorra': 6, 'Angola': 6, 'Antigua and Barbuda': 6, 'Argentina': 6, 'Armenia': 6, 'Aruba': 6, 'Australia': 6, 'Austria': 6, 'Azerbaijan': 6, '"Bahamas': 6, 'Bahrain': 6, 'Bangladesh': 6, 'Barbados': 6, 'Belarus': 6, 'Belgium': 6, 'Belize': 6, 'Benin': 6, 'Bermuda': 6, 'Bhutan': 6, 'Bolivia': 6, 'Bosnia and Herzegovina': 6, 'Botswana': 6, 'Brazil': 6, 'British Virgin Islands': 6, 'Brunei Darussalam': 3, 'Bulgaria': 3, 'Burkina Faso': 3, 'Burundi': 3, 'Cabo Verde': 3, 'Cambodia': 3, 'Cameroon': 3, 'Canada': 3, 'Cayman Islands': 3, 'Central African Republic': 3, 'Chad': 3, 'Channel Islands': 3, 'Chile': 3, 'China': 3, 'Colombia': 3, 'Comoros': 3, '"Congo': 6, 'Costa Rica': 3, "Cote d'Ivoire": 3, 'Croatia': 3, 'Cuba': 3, 'Curacao': 3, 'Cyprus': 3, 'Czech Republic': 3, 'Denmark': 3, 'Djibouti': 3, 'Dominica': 3, 'Dominican Republic': 3, 'Ecuador': 3, '"Egypt': 3, 'El Salvador': 3, 'Equatorial Guinea': 3, 'Eritrea': 3, 'Estonia': 3, 'Ethiopia': 3, 'Faroe Islands': 3, 'Fiji': 3, 'Finland': 3, 'France': 3, 'French Polynesia': 3, 'Gabon': 3, '"Gambia': 3, 'Georgia': 3, 'Germany': 3, 'Ghana': 3, 'Gibraltar': 3, 'Greece': 3, 'Greenland': 3, 'Grenada': 3, 'Guam': 3, 'Guatemala': 3, 'Guinea': 3, 'Guinea-Bissau': 3, 'Guyana': 3, 'Haiti': 3, 'Honduras': 3, '"Hong Kong SAR': 3, 'Hungary': 3, 'Iceland': 3, 'India': 3, 'Indonesia': 3, '"Iran': 3, 'Iraq': 3, 'Ireland': 3, 'Isle of Man': 3, 'Israel': 3, 'Italy': 3, 'Jamaica': 3, 'Japan': 3, 'Jordan': 3, 'Kazakhstan': 3, 'Kenya': 3, 'Kiribati': 3, '"Korea': 6, 'Kosovo': 1, 'Kuwait': 3, 'Kyrgyz Republic': 3, 'Lao PDR': 3, 'Latvia': 3, 'Lebanon': 3, 'Lesotho': 3, 'Liberia': 3, 'Libya': 3, 'Liechtenstein': 3, 'Lithuania': 3, 'Luxembourg': 3, '"Macao SAR': 3, '"Macedonia': 3, 'Madagascar': 3, 'Malawi': 3, 'Malaysia': 3, 'Maldives': 3, 'Mali': 3, 'Malta': 3, 'Marshall Islands': 3, 'Mauritania': 3, 'Mauritius': 3, 'Mexico': 3, '"Micronesia': 3, 'Moldova': 3, 'Monaco': 3, 'Mongolia': 3, 'Montenegro': 3, 'Morocco': 3, 'Mozambique': 3, 'Myanmar': 3, 'Namibia': 3, 'Nauru': 3, 'Nepal': 3, 'Netherlands': 3, 'New Caledonia': 3, 'New Zealand': 3, 'Nicaragua': 3, 'Niger': 3, 'Nigeria': 3, 'Northern Mariana Islands': 3, 'Norway': 3, 'Oman': 3, 'Pakistan': 3, 'Palau': 3, 'Panama': 3, 'Papua New Guinea': 3, 'Paraguay': 3, 'Peru': 3, 'Philippines': 3, 'Poland': 3, 'Portugal': 3, 'Puerto Rico': 3, 'Qatar': 3, 'Romania': 3, 'Russian Federation': 3, 'Rwanda': 3, 'Samoa': 3, 'San Marino': 3, 'Sao Tome and Principe': 3, 'Saudi Arabia': 3, 'Senegal': 3, 'Seychelles': 3, 'Sierra Leone': 3, 'Singapore': 3, 'Sint Maarten (Dutch part)': 2, 'Slovak Republic': 3, 'Slovenia': 3, 'Solomon Islands': 3, 'Somalia': 3, 'South Africa': 3, 'South Sudan': 3, 'Spain': 3, 'Sri Lanka': 3, 'St. Kitts and Nevis': 3, 'St. Lucia': 3, 'St. Martin (French part)': 1, 'St. Vincent and the Grenadines': 3, 'Sudan': 3, 'Suriname': 3, 'Swaziland': 3, 'Sweden': 3, 'Switzerland': 3, 'Syrian Arab Republic': 3, 'Tajikistan': 3, 'Tanzania': 3, 'Thailand': 3, 'Timor-Leste': 3, 'Togo': 3, 'Tonga': 3, 'Trinidad and Tobago': 3, 'Tunisia': 3, 'Turkey': 3, 'Turkmenistan': 3, 'Turks and Caicos Islands': 3, 'Tuvalu': 3, 'Uganda': 3, 'Ukraine': 3, 'United Arab Emirates': 3, 'United Kingdom': 3, 'United States': 3, 'Uruguay': 3, 'Uzbekistan': 3, 'Vanuatu': 3, '"Venezuela': 3, 'Vietnam': 3, 'Virgin Islands (U.S.)': 3, '"Yemen': 3, 'Zambia': 3, 'Zimbabwe': 3}
## Country Name,Country Code,Indicator Name,Indicator Code,year,value
## 
## Arab World,ARB,"Population, total",SP.POP.TOTL,1960,92496099.0
## 
## Arab World,ARB,Rural population (% of total population),SP.RUR.TOTL.ZS,1960,68.7081520885329
## 
## {'Country Name': 1, 'Arab World': 168, 'Caribbean small states': 168, 'Central Europe and the Baltics': 168, 'Early-demographic dividend': 168, 'East Asia & Pacific': 168, 'East Asia & Pacific (excluding high income)': 168, 'East Asia & Pacific (IDA & IBRD countries)': 168, 'Euro area': 168, 'Europe & Central Asia': 168, 'Europe & Central Asia (excluding high income)': 168, 'Europe & Central Asia (IDA & IBRD countries)': 168, 'European Union': 168, 'Fragile and conflict affected situations': 168, 'Heavily indebted poor countries (HIPC)': 168, 'High income': 168, 'IBRD only': 168, 'IDA & IBRD total': 168, 'IDA blend': 168, 'IDA only': 168, 'IDA total': 168, 'Late-demographic dividend': 168, 'Latin America & Caribbean': 168, 'Latin America & Caribbean (excluding high income)': 168, 'Latin America & the Caribbean (IDA & IBRD countries)': 168, 'Least developed countries: UN classification': 168, 'Low & middle income': 168, 'Low income': 168, 'Lower middle income': 168, 'Middle East & North Africa': 168, 'Middle East & North Africa (excluding high income)': 168, 'Middle East & North Africa (IDA & IBRD countries)': 168, 'Middle income': 168, 'North America': 168, 'OECD members': 168, 'Other small states': 168, 'Pacific island small states': 168, 'Post-demographic dividend': 168, 'Pre-demographic dividend': 168, 'Small states': 168, 'South Asia': 168, 'South Asia (IDA & IBRD)': 168, 'Sub-Saharan Africa': 168, 'Sub-Saharan Africa (excluding high income)': 168, 'Sub-Saharan Africa (IDA & IBRD countries)': 168, 'Upper middle income': 168, 'World': 168, 'Afghanistan': 168, 'Albania': 168, 'Algeria': 168, 'American Samoa': 168, 'Andorra': 168, 'Angola': 168, 'Antigua and Barbuda': 168, 'Argentina': 168, 'Armenia': 168, 'Aruba': 168, 'Australia': 168, 'Austria': 168, 'Azerbaijan': 168, '"Bahamas': 168, 'Bahrain': 168, 'Bangladesh': 168, 'Barbados': 168, 'Belarus': 168, 'Belgium': 168, 'Belize': 168, 'Benin': 168, 'Bermuda': 168, 'Bhutan': 168, 'Bolivia': 168, 'Bosnia and Herzegovina': 168, 'Botswana': 168, 'Brazil': 168, 'British Virgin Islands': 168, 'Brunei Darussalam': 168, 'Bulgaria': 168, 'Burkina Faso': 168, 'Burundi': 168, 'Cabo Verde': 168, 'Cambodia': 168, 'Cameroon': 168, 'Canada': 168, 'Cayman Islands': 168, 'Central African Republic': 168, 'Chad': 168, 'Channel Islands': 168, 'Chile': 168, 'China': 168, 'Colombia': 168, 'Comoros': 168, '"Congo': 336, 'Costa Rica': 168, "Cote d'Ivoire": 168, 'Croatia': 168, 'Cuba': 168, 'Curacao': 168, 'Cyprus': 168, 'Czech Republic': 168, 'Denmark': 168, 'Djibouti': 168, 'Dominica': 168, 'Dominican Republic': 168, 'Ecuador': 168, '"Egypt': 168, 'El Salvador': 168, 'Equatorial Guinea': 168, 'Eritrea': 156, 'Estonia': 168, 'Ethiopia': 168, 'Faroe Islands': 168, 'Fiji': 168, 'Finland': 168, 'France': 168, 'French Polynesia': 168, 'Gabon': 168, '"Gambia': 168, 'Georgia': 168, 'Germany': 168, 'Ghana': 168, 'Gibraltar': 168, 'Greece': 168, 'Greenland': 168, 'Grenada': 168, 'Guam': 168, 'Guatemala': 168, 'Guinea': 168, 'Guinea-Bissau': 168, 'Guyana': 168, 'Haiti': 168, 'Honduras': 168, '"Hong Kong SAR': 168, 'Hungary': 168, 'Iceland': 168, 'India': 168, 'Indonesia': 168, '"Iran': 168, 'Iraq': 168, 'Ireland': 168, 'Isle of Man': 168, 'Israel': 168, 'Italy': 168, 'Jamaica': 168, 'Japan': 168, 'Jordan': 168, 'Kazakhstan': 168, 'Kenya': 168, 'Kiribati': 168, '"Korea': 336, 'Kosovo': 56, 'Kuwait': 165, 'Kyrgyz Republic': 168, 'Lao PDR': 168, 'Latvia': 168, 'Lebanon': 168, 'Lesotho': 168, 'Liberia': 168, 'Libya': 168, 'Liechtenstein': 168, 'Lithuania': 168, 'Luxembourg': 168, '"Macao SAR': 168, '"Macedonia': 168, 'Madagascar': 168, 'Malawi': 168, 'Malaysia': 168, 'Maldives': 168, 'Mali': 168, 'Malta': 168, 'Marshall Islands': 168, 'Mauritania': 168, 'Mauritius': 168, 'Mexico': 168, '"Micronesia': 168, 'Moldova': 168, 'Monaco': 168, 'Mongolia': 168, 'Montenegro': 168, 'Morocco': 168, 'Mozambique': 168, 'Myanmar': 168, 'Namibia': 168, 'Nauru': 168, 'Nepal': 168, 'Netherlands': 168, 'New Caledonia': 168, 'New Zealand': 168, 'Nicaragua': 168, 'Niger': 168, 'Nigeria': 168, 'Northern Mariana Islands': 168, 'Norway': 168, 'Oman': 168, 'Pakistan': 168, 'Palau': 168, 'Panama': 168, 'Papua New Guinea': 168, 'Paraguay': 168, 'Peru': 168, 'Philippines': 168, 'Poland': 168, 'Portugal': 168, 'Puerto Rico': 168, 'Qatar': 168, 'Romania': 168, 'Russian Federation': 168, 'Rwanda': 168, 'Samoa': 168, 'San Marino': 168, 'Sao Tome and Principe': 168, 'Saudi Arabia': 168, 'Senegal': 168, 'Seychelles': 168, 'Sierra Leone': 168, 'Singapore': 168, 'Sint Maarten (Dutch part)': 130, 'Slovak Republic': 168, 'Slovenia': 168, 'Solomon Islands': 168, 'Somalia': 168, 'South Africa': 168, 'South Sudan': 168, 'Spain': 168, 'Sri Lanka': 168, 'St. Kitts and Nevis': 168, 'St. Lucia': 168, 'St. Martin (French part)': 56, 'St. Vincent and the Grenadines': 168, 'Sudan': 168, 'Suriname': 168, 'Swaziland': 168, 'Sweden': 168, 'Switzerland': 168, 'Syrian Arab Republic': 168, 'Tajikistan': 168, 'Tanzania': 168, 'Thailand': 168, 'Timor-Leste': 168, 'Togo': 168, 'Tonga': 168, 'Trinidad and Tobago': 168, 'Tunisia': 168, 'Turkey': 168, 'Turkmenistan': 168, 'Turks and Caicos Islands': 168, 'Tuvalu': 168, 'Uganda': 168, 'Ukraine': 168, 'United Arab Emirates': 168, 'United Kingdom': 168, 'United States': 168, 'Uruguay': 168, 'Uzbekistan': 168, 'Vanuatu': 168, '"Venezuela': 168, 'Vietnam': 168, 'Virgin Islands (U.S.)': 168, '"Yemen': 168, 'Zambia': 168, 'Zimbabwe': 168, 'Serbia': 78, 'West Bank and Gaza': 78}
##                      Country Name Country Code  \
## 0                      Arab World          ARB   
## 1                      Arab World          ARB   
## 2                      Arab World          ARB   
## 3          Caribbean small states          CSS   
## 4          Caribbean small states          CSS   
## 5          Caribbean small states          CSS   
## 6  Central Europe and the Baltics          CEB   
## 7  Central Europe and the Baltics          CEB   
## 8  Central Europe and the Baltics          CEB   
## 9      Early-demographic dividend          EAR   
## 
##                              Indicator Name     Indicator Code  year  \
## 0                         Population, total        SP.POP.TOTL  1960   
## 1  Rural population (% of total population)     SP.RUR.TOTL.ZS  1960   
## 2             Urban population (% of total)  SP.URB.TOTL.IN.ZS  1960   
## 3                         Population, total        SP.POP.TOTL  1960   
## 4  Rural population (% of total population)     SP.RUR.TOTL.ZS  1960   
## 5             Urban population (% of total)  SP.URB.TOTL.IN.ZS  1960   
## 6                         Population, total        SP.POP.TOTL  1960   
## 7  Rural population (% of total population)     SP.RUR.TOTL.ZS  1960   
## 8             Urban population (% of total)  SP.URB.TOTL.IN.ZS  1960   
## 9                         Population, total        SP.POP.TOTL  1960   
## 
##           value  
## 0  9.249610e+07  
## 1  6.870815e+01  
## 2  3.129185e+01  
## 3  4.192721e+06  
## 4  6.840152e+01  
## 5  3.159848e+01  
## 6  9.140158e+07  
## 7  5.549208e+01  
## 8  4.450792e+01  
## 9  9.800680e+08  
##                                    Country Name Country Code  \
## 10                   Early-demographic dividend          EAR   
## 11                   Early-demographic dividend          EAR   
## 12                          East Asia & Pacific          EAS   
## 13                          East Asia & Pacific          EAS   
## 14                          East Asia & Pacific          EAS   
## 15  East Asia & Pacific (excluding high income)          EAP   
## 16  East Asia & Pacific (excluding high income)          EAP   
## 17  East Asia & Pacific (excluding high income)          EAP   
## 18   East Asia & Pacific (IDA & IBRD countries)          TEA   
## 19   East Asia & Pacific (IDA & IBRD countries)          TEA   
## 
##                               Indicator Name     Indicator Code  year  \
## 10  Rural population (% of total population)     SP.RUR.TOTL.ZS  1960   
## 11             Urban population (% of total)  SP.URB.TOTL.IN.ZS  1960   
## 12                         Population, total        SP.POP.TOTL  1960   
## 13  Rural population (% of total population)     SP.RUR.TOTL.ZS  1960   
## 14             Urban population (% of total)  SP.URB.TOTL.IN.ZS  1960   
## 15                         Population, total        SP.POP.TOTL  1960   
## 16  Rural population (% of total population)     SP.RUR.TOTL.ZS  1960   
## 17             Urban population (% of total)  SP.URB.TOTL.IN.ZS  1960   
## 18                         Population, total        SP.POP.TOTL  1960   
## 19  Rural population (% of total population)     SP.RUR.TOTL.ZS  1960   
## 
##            value  
## 10  7.705007e+01  
## 11  2.294993e+01  
## 12  1.042480e+09  
## 13  7.752853e+01  
## 14  2.247147e+01  
## 15  8.964930e+08  
## 16  8.308232e+01  
## 17  1.691768e+01  
## 18  8.850532e+08  
## 19  8.338348e+01  
##    CountryName CountryCode  Year  Total Population  \
## 0  Afghanistan         AFG  1960         8994793.0   
## 1  Afghanistan         AFG  1961         9164945.0   
## 2  Afghanistan         AFG  1962         9343772.0   
## 3  Afghanistan         AFG  1963         9531555.0   
## 4  Afghanistan         AFG  1964         9728645.0   
## 
##    Urban population (% of total)  
## 0                          8.221  
## 1                          8.508  
## 2                          8.805  
## 3                          9.110  
## 4                          9.426  
## [(91401583.0, 44.507921139002597), (92237118.0, 45.206665319194002), (93014890.0, 45.866564696018003), (93845749.0, 46.5340927663649), (94722599.0, 47.208742980352604), (95447065.0, 47.8803084429574), (96148635.0, 48.505097191759397), (97043587.0, 49.067767135854098), (97882394.0, 49.638696249807701), (98602140.0, 50.215657693321887), (99133296.0, 50.780409860456999), (99638983.0, 51.429566445052899), (100363597.0, 52.162105936757101), (101120519.0, 52.894471471541799), (101946256.0, 53.627174447338199), (102862489.0, 54.349653085382698), (103770134.0, 55.061127012228795), (104589313.0, 55.7886862473798), (105304312.0, 56.530668389657201), (105924838.0, 57.213134522150497), (106564905.0, 57.822931161135998), (107187982.0, 58.286690506739795), (107770794.0, 58.683563322897996), (108326895.0, 59.081567030459006), (108853181.0, 59.480212620603197), (109360296.0, 59.873735202774107), (109847148.0, 60.258086533789701), (110296680.0, 60.638613615994608), (110688533.0, 61.020488525916214), (110801380.0, 61.312199620203295), (110745760.0, 61.520994481657802), (110290445.0, 61.741539221625203), (110005636.0, 61.820287894431203), (110081461.0, 61.779410244518786), (110019570.0, 61.751130812191001), (109913216.0, 61.715962603505297), (109563097.0, 61.695812920403299), (109459093.0, 61.661656630381501), (109207205.0, 61.632890822478196), (109092730.0, 61.595078134840001), (108405522.0, 61.567439264127209), (107800399.0, 61.571020971323101), (107097577.0, 61.629953932533901), (106760768.0, 61.670694008505109), (106466116.0, 61.711606934595004), (106173766.0, 61.7605094345057), (105901322.0, 61.815522500550095), (105504531.0, 61.887634194914291), (105126686.0, 61.964992935380899), (104924372.0, 62.020159705764101), (104543801.0, 62.059416833265885), (104174038.0, 62.099517041078904), (103935318.0, 62.141847349338995), (103713726.0, 62.197640397588302), (103496179.0, 62.269282909458894), (103256779.0, 62.357002383678797)]

Plots 20 and 21 are not displayed as they are redundant with plot 22.

Urban Population by Year for Country Code CEB:

Urban Population by Year for Country Code ARB:

Network Analysis in Python (Part I)

Chapter 1 - Introduction to Networks

Introduction to networks - examples like social networks, transportation networks, etc.:

• Networks are a useful tool for modeling relationships between entities
  
• Networks are defined by two sets of attributes; nodes and edges (these form a network, known in mathematics as a “graph”)
  
• The “networkx” library is frequently imported as nx
  
• The start of an empty network (Graph) can be defined as G = nx.Graph()
  
• Nodes can be added using G.add_nodes_from([nodeList])
  • The call to G.nodes() provides a list of the nodes currently in the Graph
    
• The call to G.add_edge(myTuple) will create a link (edge) as defined by myTuple
  • The call to G.edges will be a tuple showing all the edges currently defined
    
• Metadata can further be added to the nodes, such as using G.node[1][“label”] = “blue”
  • The call to G.nodes(data=True) will then bring back the nodes and also the associated metadata (as dictionaries)
    
• The nx.draw() function will draw out the Graph (requires plt.show() where plt is matplotlab.pyplot)

Types of graphs:

• Undirected graphs (e.g., Facebook) are typically drawn as a line with no arrows between two circles
  • These are created empty as per above using nx.Graph()
    
• Directed graphs (e.g., Twitter) are typically drawn as a line with an arrow (uni or bi directional depending on follow/follower) between two circles
  • These are created empty using nx.DiGraph()
    
• Multi graphs (e.g., trips between bike stations) are typically drawn as many arrows between two circles
  • These are created empty using nx.MultiGraph()
    
• Multi graphs can instead be created as weighted arrows, where the weight represents the frequency of occurrence (save memory, plotting, etc. vs. baseline)
  • The weight may just be included as part of the metadata dictionary
    
• Self-loops are nodes that connect to themselves, such as bike trips that start and end at the same station

Network visualization - irrational (“looks like a hairball”) and rational visualizations:

• Three primary types of plots - Matrix plot, Arc plot, Circos plot
  
• The Matrix plot is a simple row-column, with the square filled in if the edge between the nodes exists
  • With an undirected Graph, the matrix will be symmetrical around the diagonal
    
  • With a directed Graph, the matrix need not be symmetrical around the diagonal (the columns are what the arrow hits, the rows are what it is from)
    
• The Arc plot is a transformation where the nodes are all along a single axis of the plot, with connections drawn as semi-circles
  
• The Circos plot is a trasnformation of the Arc plot, but where the “axis” is converted in to a circle
  
• The “nxviz” package, typically imported as “nv”, allows for visualizing the Graphs the above types
  • ap = nv.ArcPlot(G) ; ap.draw() ; plt.show() will create the ArcPlot

Example code includes:

## NEED TO MOCK UP T_sub from the above
import networkx as nx
import datetime

T_sub = nx.DiGraph()

T_sub.add_nodes_from([1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

T_sub.add_edges_from([(1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (1, 13), (1, 14), (1, 15), (1, 16), (1, 17), (1, 18), (1, 19), (1, 20), (1, 21), (1, 22), (1, 23), (1, 24), (1, 25), (1, 26), (1, 27), (1, 28), (1, 29), (1, 30), (1, 31), (1, 32), (1, 33), (1, 34), (1, 35), (1, 36), (1, 37), (1, 38), (1, 39), (1, 40), (1, 41), (1, 42), (1, 43), (1, 44), (1, 45), (1, 46), (1, 47), (1, 48), (1, 49), (16, 48), (16, 18), (16, 35), (16, 36), (18, 16), (18, 24), (18, 35), (18, 36), (19, 35), (19, 36), (19, 5), (19, 8), (19, 11), (19, 13), (19, 15), (19, 48), (19, 17), (19, 20), (19, 21), (19, 24), (19, 37), (19, 30), (19, 31), (28, 1), (28, 5), (28, 7), (28, 8), (28, 11), (28, 14), (28, 15), (28, 17), (28, 20), (28, 21), (28, 24), (28, 25), (28, 27), (28, 29), (28, 30), (28, 31), (28, 35), (28, 36), (28, 37), (28, 44), (28, 48), (28, 49), (36, 24), (36, 35), (36, 5), (36, 37), (37, 24), (37, 35), (37, 36), (39, 1), (39, 35), (39, 36), (39, 38), (39, 33), (39, 40), (39, 41), (39, 45), (39, 24), (42, 1), (43, 48), (43, 35), (43, 36), (43, 37), (43, 24), (43, 29), (43, 47), (45, 1), (45, 39), (45, 41)])

node_meta = [{'occupation': 'scientist', 'category': 'I'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'scientist', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'scientist', 'category': 'D'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'P'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'scientist', 'category': 'D'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'P'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'P'}]

for x in range(len(T_sub.nodes())) :
    T_sub.node[T_sub.nodes()[x]]["occupation"] = node_meta[x]["occupation"]
    T_sub.node[T_sub.nodes()[x]]["category"] = node_meta[x]["category"]

edge_meta = [{'date': datetime.date(2012, 11, 17)}, {'date': datetime.date(2007, 6, 19)}, {'date': datetime.date(2014, 3, 18)}, {'date': datetime.date(2007, 3, 18)}, {'date': datetime.date(2011, 12, 19)}, {'date': datetime.date(2013, 12, 7)}, {'date': datetime.date(2009, 11, 9)}, {'date': datetime.date(2008, 10, 7)}, {'date': datetime.date(2008, 8, 14)}, {'date': datetime.date(2011, 3, 22)}, {'date': datetime.date(2014, 8, 3)}, {'date': datetime.date(2007, 5, 19)}, {'date': datetime.date(2009, 12, 13)}, {'date': datetime.date(2011, 4, 7)}, {'date': datetime.date(2013, 8, 2)}, {'date': datetime.date(2014, 11, 17)}, {'date': datetime.date(2013, 5, 20)}, {'date': datetime.date(2010, 12, 15)}, {'date': datetime.date(2010, 11, 27)}, {'date': datetime.date(2013, 9, 5)}, {'date': datetime.date(2013, 3, 1)}, {'date': datetime.date(2007, 7, 8)}, {'date': datetime.date(2010, 5, 23)}, {'date': datetime.date(2007, 9, 14)}, {'date': datetime.date(2013, 1, 24)}, {'date': datetime.date(2013, 6, 21)}, {'date': datetime.date(2010, 6, 28)}, {'date': datetime.date(2011, 12, 2)}, {'date': datetime.date(2010, 7, 24)}, {'date': datetime.date(2010, 7, 4)}, {'date': datetime.date(2013, 9, 28)}, {'date': datetime.date(2007, 3, 17)}, {'date': datetime.date(2013, 11, 7)}, {'date': datetime.date(2012, 8, 13)}, {'date': datetime.date(2009, 2, 19)}, {'date': datetime.date(2007, 3, 17)}, {'date': datetime.date(2011, 11, 15)}, {'date': datetime.date(2011, 12, 26)}, {'date': datetime.date(2010, 2, 14)}, {'date': datetime.date(2014, 4, 16)}, {'date': datetime.date(2010, 2, 28)}, {'date': datetime.date(2007, 11, 2)}, {'date': datetime.date(2008, 5, 17)}, {'date': datetime.date(2013, 11, 18)}, {'date': datetime.date(2010, 11, 14)}, {'date': datetime.date(2007, 8, 19)}, {'date': datetime.date(2012, 5, 11)}, {'date': datetime.date(2007, 10, 27)}, {'date': datetime.date(2009, 11, 14)}, {'date': datetime.date(2009, 4, 19)}, {'date': datetime.date(2007, 7, 14)}, {'date': datetime.date(2012, 5, 7)}, {'date': datetime.date(2014, 5, 4)}, {'date': datetime.date(2012, 6, 16)}, {'date': datetime.date(2012, 4, 25)}, {'date': datetime.date(2012, 6, 25)}, {'date': datetime.date(2010, 10, 14)}, {'date': datetime.date(2013, 4, 18)}, {'date': datetime.date(2013, 10, 6)}, {'date': datetime.date(2009, 8, 2)}, {'date': datetime.date(2008, 9, 23)}, {'date': datetime.date(2011, 11, 26)}, {'date': datetime.date(2010, 1, 22)}, {'date': datetime.date(2012, 6, 23)}, {'date': datetime.date(2013, 11, 20)}, {'date': datetime.date(2008, 7, 6)}, {'date': datetime.date(2009, 4, 12)}, {'date': datetime.date(2011, 12, 28)}, {'date': datetime.date(2012, 1, 22)}, {'date': datetime.date(2009, 1, 26)}, {'date': datetime.date(2012, 1, 13)}, {'date': datetime.date(2010, 9, 26)}, {'date': datetime.date(2013, 11, 14)}, {'date': datetime.date(2010, 7, 22)}, {'date': datetime.date(2013, 3, 17)}, {'date': datetime.date(2008, 10, 18)}, {'date': datetime.date(2008, 12, 9)}, {'date': datetime.date(2012, 1, 14)}, {'date': datetime.date(2012, 6, 28)}, {'date': datetime.date(2011, 10, 5)}, {'date': datetime.date(2007, 5, 19)}, {'date': datetime.date(2013, 1, 24)}, {'date': datetime.date(2008, 6, 28)}, {'date': datetime.date(2008, 5, 16)}, {'date': datetime.date(2013, 5, 8)}, {'date': datetime.date(2007, 7, 23)}, {'date': datetime.date(2010, 8, 4)}, {'date': datetime.date(2011, 10, 18)}, {'date': datetime.date(2011, 6, 2)}, {'date': datetime.date(2009, 5, 23)}, {'date': datetime.date(2010, 10, 14)}, {'date': datetime.date(2013, 7, 17)}, {'date': datetime.date(2008, 5, 19)}, {'date': datetime.date(2008, 3, 19)}, {'date': datetime.date(2010, 8, 14)}, {'date': datetime.date(2012, 6, 19)}, {'date': datetime.date(2013, 8, 12)}, {'date': datetime.date(2013, 7, 6)}, {'date': datetime.date(2014, 10, 11)}, {'date': datetime.date(2012, 7, 1)}, {'date': datetime.date(2013, 11, 5)}, {'date': datetime.date(2009, 11, 6)}, {'date': datetime.date(2009, 4, 19)}, {'date': datetime.date(2008, 8, 12)}, {'date': datetime.date(2012, 8, 8)}, {'date': datetime.date(2009, 8, 12)}, {'date': datetime.date(2012, 5, 27)}, {'date': datetime.date(2011, 9, 15)}, {'date': datetime.date(2013, 12, 19)}, {'date': datetime.date(2007, 12, 7)}, {'date': datetime.date(2008, 3, 4)}, {'date': datetime.date(2013, 9, 16)}, {'date': datetime.date(2009, 11, 22)}, {'date': datetime.date(2014, 9, 19)}, {'date': datetime.date(2008, 10, 20)}, {'date': datetime.date(2010, 12, 16)}, {'date': datetime.date(2013, 3, 15)}, {'date': datetime.date(2012, 4, 25)}, {'date': datetime.date(2009, 5, 10)}]

for x in range(len(T_sub.edges())) :
    a, b = T_sub.edges()[x]
    T_sub.edge[a][b]["date"] = edge_meta[x]["date"]


# Import necessary modules
import matplotlib.pyplot as plt


# Draw the graph to screen
nx.draw(T_sub)
# plt.show()
plt.savefig("_dummyPy024.png", bbox_inches="tight")



# Also need to mock up T
# Use T_sub for these
# Use a list comprehension to get the nodes of interest: noi
noi = [n for n, d in T_sub.nodes(data=True) if d['occupation'] == 'scientist']

# Use a list comprehension to get the edges of interest: eoi
eoi = [(u, v) for u, v, d in T_sub.edges(data=True) if d["date"] < datetime.date(2010, 1, 1)]


# Set the weight of the edge
T_sub.edge[1][10]["weight"] = 2

# Iterate over all the edges (with metadata)
for u, v, d in T_sub.edges(data=True):
    
    # Check if node 293 is involved
    # Make it node 23 instead
    if 23 in [u, v]:
        # Set the weight to 1.1
        T_sub.edge[u][v]["weight"] = 1.1


# Define find_selfloop_nodes()
def find_selfloop_nodes(G):
    """
    Finds all nodes that have self-loops in the graph G.
    """
    nodes_in_selfloops = []
    
    # Iterate over all the edges of G
    for u, v in G.edges():
    # Check if node u and node v are the same
        if u == v:
            # Append node u to nodes_in_selfloops
            nodes_in_selfloops.append(u)
            
    return nodes_in_selfloops

# Check whether number of self loops equals the number of nodes in self loops
# The mock-up above has no self-loops, so this is just for reference on how to find them
assert T_sub.number_of_selfloops() == len(find_selfloop_nodes(T_sub))


# Import nxviz
import nxviz as nv

# Create the MatrixPlot object: m
m = nv.MatrixPlot(T_sub)

# Draw m to the screen
m.draw()

# Display the plot
# plt.show()
plt.savefig("_dummyPy025.png", bbox_inches="tight")


# Convert T to a matrix format: A
A = nx.to_numpy_matrix(T_sub)

# Convert A back to the NetworkX form as a directed graph: T_conv
T_conv = nx.from_numpy_matrix(A, create_using=nx.DiGraph())

# Check that the `category` metadata field is lost from each node
for n, d in T_conv.nodes(data=True):
    assert 'category' not in d.keys()


# Import necessary modules
import matplotlib.pyplot as plt
from nxviz import CircosPlot

# Create the CircosPlot object: c
c = CircosPlot(T_sub)

# Draw c to the screen
c.draw()

# Display the plot
# plt.show()
plt.savefig("_dummyPy026.png", bbox_inches="tight")


# Import necessary modules
from nxviz import ArcPlot

# Create the un-customized ArcPlot object: a
a = ArcPlot(T_sub)

# Draw a to the screen
a.draw()

# Display the plot
# plt.show()
plt.savefig("_dummyPy027.png", bbox_inches="tight")


# Create the customized ArcPlot object: a2
a2 = ArcPlot(T_sub, node_order="category", node_color="category")

# Draw a2 to the screen
a2.draw()

# Display the plot
# plt.show()
plt.savefig("_dummyPy028.png", bbox_inches="tight")

## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\networkx\drawing\nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.
##     Future behavior will be consistent with the long-time default:
##     plot commands add elements without first clearing the
##     Axes and/or Figure.
##   b = plt.ishold()
## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\networkx\drawing\nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.
##     Future behavior will be consistent with the long-time default:
##     plot commands add elements without first clearing the
##     Axes and/or Figure.
##   plt.hold(b)
## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\matplotlib\__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.
##   warnings.warn(self.msg_depr_set % key)
## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\matplotlib\rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0
##   warnings.warn("axes.hold is deprecated, will be removed in 3.0")

Example network plot:

Example MatrixPlot (network):

Example CircosPlot (network):

Example ArcPlot (network):

Example ArcPlot (network) colored by category:

---

Chapter 2 - Important Nodes

Degree centrality - one method of determining important nodes:

• Being connected to another node makes you a “neighbor” of that node
  
• Degree centrality for a node is defined as “# neighbors I have” divided by “number of possible neighbors”
  • Depending on self-loops, the “number of possible neighbors” may or may not include itself
    
• Examples of high degree centrality include Twitter broadcasters, airport hubs, disease super-spreaders, and the like
  
• Within the “networkx” package, G.neighbors(1) will give a list of all the neighbors of node 1
  • Can instead run nx.degree_centrality(G) # outputs a dictionary of node:centrality; self-loops are not considered

Graph algorithms - path finding for optimization (e.g., shortest path between nodes, information or disease spread, etc.):

• Breadth-first search (BFS) algorithm first developed in the 1950s for finding the shortest path out of a maze
  
• Basically, take one of the points, then find its neighbors, then its neighbors’ neighbors, etc., until the second point is found
  
• Use G.edges(), G.nodes(), and automate the search for finding paths between any two given points

Betweeness centrality - including the key concept of “all shortest paths”:

• All shortest paths is based on finding all shortest paths between all pairs of nodes
  
• Betweeness centrality is defined as “# shortest paths running THROUGH node” divide by “all possible shortest paths”
  • n.b. that a node and its neighbor is not counted in the numerator or the denominator; only paths of length 2+ are relevant
    
• This helps to identify “bottleneck” nodes - points that if eliminated would significantly slow or even stop connections
  • Can use nx.barbell_graph(m1=, m2=) # m1 will be the size of the barbells, m2 will be the number of connector nodes (zero would just connect a point on each dumbbell)
    
  • Can use nx.betweenness_centrality(G) to get a dictionary of node:betweeness

Example code includes:

import networkx as nx
import matplotlib.pyplot as plt
import datetime


# DO NOT HAVE Graph T
# Make the same as above
T = nx.DiGraph()

T.add_nodes_from([1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

T.add_edges_from([(1, 3), (1, 4), (1, 5), (1, 6), (1, 7), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (1, 13), (1, 14), (1, 15), (1, 16), (1, 17), (1, 18), (1, 19), (1, 20), (1, 21), (1, 22), (1, 23), (1, 24), (1, 25), (1, 26), (1, 27), (1, 28), (1, 29), (1, 30), (1, 31), (1, 32), (1, 33), (1, 34), (1, 35), (1, 36), (1, 37), (1, 38), (1, 39), (1, 40), (1, 41), (1, 42), (1, 43), (1, 44), (1, 45), (1, 46), (1, 47), (1, 48), (1, 49), (16, 48), (16, 18), (16, 35), (16, 36), (18, 16), (18, 24), (18, 35), (18, 36), (19, 35), (19, 36), (19, 5), (19, 8), (19, 11), (19, 13), (19, 15), (19, 48), (19, 17), (19, 20), (19, 21), (19, 24), (19, 37), (19, 30), (19, 31), (28, 1), (28, 5), (28, 7), (28, 8), (28, 11), (28, 14), (28, 15), (28, 17), (28, 20), (28, 21), (28, 24), (28, 25), (28, 27), (28, 29), (28, 30), (28, 31), (28, 35), (28, 36), (28, 37), (28, 44), (28, 48), (28, 49), (36, 24), (36, 35), (36, 5), (36, 37), (37, 24), (37, 35), (37, 36), (39, 1), (39, 35), (39, 36), (39, 38), (39, 33), (39, 40), (39, 41), (39, 45), (39, 24), (42, 1), (43, 48), (43, 35), (43, 36), (43, 37), (43, 24), (43, 29), (43, 47), (45, 1), (45, 39), (45, 41)])

node_meta = [{'occupation': 'scientist', 'category': 'I'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'scientist', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'scientist', 'category': 'D'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'P'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'scientist', 'category': 'D'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'P'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'P'}]

for x in range(len(T.nodes())) :
    T.node[T.nodes()[x]]["occupation"] = node_meta[x]["occupation"]
    T.node[T.nodes()[x]]["category"] = node_meta[x]["category"]

edge_meta = [{'date': datetime.date(2012, 11, 17)}, {'date': datetime.date(2007, 6, 19)}, {'date': datetime.date(2014, 3, 18)}, {'date': datetime.date(2007, 3, 18)}, {'date': datetime.date(2011, 12, 19)}, {'date': datetime.date(2013, 12, 7)}, {'date': datetime.date(2009, 11, 9)}, {'date': datetime.date(2008, 10, 7)}, {'date': datetime.date(2008, 8, 14)}, {'date': datetime.date(2011, 3, 22)}, {'date': datetime.date(2014, 8, 3)}, {'date': datetime.date(2007, 5, 19)}, {'date': datetime.date(2009, 12, 13)}, {'date': datetime.date(2011, 4, 7)}, {'date': datetime.date(2013, 8, 2)}, {'date': datetime.date(2014, 11, 17)}, {'date': datetime.date(2013, 5, 20)}, {'date': datetime.date(2010, 12, 15)}, {'date': datetime.date(2010, 11, 27)}, {'date': datetime.date(2013, 9, 5)}, {'date': datetime.date(2013, 3, 1)}, {'date': datetime.date(2007, 7, 8)}, {'date': datetime.date(2010, 5, 23)}, {'date': datetime.date(2007, 9, 14)}, {'date': datetime.date(2013, 1, 24)}, {'date': datetime.date(2013, 6, 21)}, {'date': datetime.date(2010, 6, 28)}, {'date': datetime.date(2011, 12, 2)}, {'date': datetime.date(2010, 7, 24)}, {'date': datetime.date(2010, 7, 4)}, {'date': datetime.date(2013, 9, 28)}, {'date': datetime.date(2007, 3, 17)}, {'date': datetime.date(2013, 11, 7)}, {'date': datetime.date(2012, 8, 13)}, {'date': datetime.date(2009, 2, 19)}, {'date': datetime.date(2007, 3, 17)}, {'date': datetime.date(2011, 11, 15)}, {'date': datetime.date(2011, 12, 26)}, {'date': datetime.date(2010, 2, 14)}, {'date': datetime.date(2014, 4, 16)}, {'date': datetime.date(2010, 2, 28)}, {'date': datetime.date(2007, 11, 2)}, {'date': datetime.date(2008, 5, 17)}, {'date': datetime.date(2013, 11, 18)}, {'date': datetime.date(2010, 11, 14)}, {'date': datetime.date(2007, 8, 19)}, {'date': datetime.date(2012, 5, 11)}, {'date': datetime.date(2007, 10, 27)}, {'date': datetime.date(2009, 11, 14)}, {'date': datetime.date(2009, 4, 19)}, {'date': datetime.date(2007, 7, 14)}, {'date': datetime.date(2012, 5, 7)}, {'date': datetime.date(2014, 5, 4)}, {'date': datetime.date(2012, 6, 16)}, {'date': datetime.date(2012, 4, 25)}, {'date': datetime.date(2012, 6, 25)}, {'date': datetime.date(2010, 10, 14)}, {'date': datetime.date(2013, 4, 18)}, {'date': datetime.date(2013, 10, 6)}, {'date': datetime.date(2009, 8, 2)}, {'date': datetime.date(2008, 9, 23)}, {'date': datetime.date(2011, 11, 26)}, {'date': datetime.date(2010, 1, 22)}, {'date': datetime.date(2012, 6, 23)}, {'date': datetime.date(2013, 11, 20)}, {'date': datetime.date(2008, 7, 6)}, {'date': datetime.date(2009, 4, 12)}, {'date': datetime.date(2011, 12, 28)}, {'date': datetime.date(2012, 1, 22)}, {'date': datetime.date(2009, 1, 26)}, {'date': datetime.date(2012, 1, 13)}, {'date': datetime.date(2010, 9, 26)}, {'date': datetime.date(2013, 11, 14)}, {'date': datetime.date(2010, 7, 22)}, {'date': datetime.date(2013, 3, 17)}, {'date': datetime.date(2008, 10, 18)}, {'date': datetime.date(2008, 12, 9)}, {'date': datetime.date(2012, 1, 14)}, {'date': datetime.date(2012, 6, 28)}, {'date': datetime.date(2011, 10, 5)}, {'date': datetime.date(2007, 5, 19)}, {'date': datetime.date(2013, 1, 24)}, {'date': datetime.date(2008, 6, 28)}, {'date': datetime.date(2008, 5, 16)}, {'date': datetime.date(2013, 5, 8)}, {'date': datetime.date(2007, 7, 23)}, {'date': datetime.date(2010, 8, 4)}, {'date': datetime.date(2011, 10, 18)}, {'date': datetime.date(2011, 6, 2)}, {'date': datetime.date(2009, 5, 23)}, {'date': datetime.date(2010, 10, 14)}, {'date': datetime.date(2013, 7, 17)}, {'date': datetime.date(2008, 5, 19)}, {'date': datetime.date(2008, 3, 19)}, {'date': datetime.date(2010, 8, 14)}, {'date': datetime.date(2012, 6, 19)}, {'date': datetime.date(2013, 8, 12)}, {'date': datetime.date(2013, 7, 6)}, {'date': datetime.date(2014, 10, 11)}, {'date': datetime.date(2012, 7, 1)}, {'date': datetime.date(2013, 11, 5)}, {'date': datetime.date(2009, 11, 6)}, {'date': datetime.date(2009, 4, 19)}, {'date': datetime.date(2008, 8, 12)}, {'date': datetime.date(2012, 8, 8)}, {'date': datetime.date(2009, 8, 12)}, {'date': datetime.date(2012, 5, 27)}, {'date': datetime.date(2011, 9, 15)}, {'date': datetime.date(2013, 12, 19)}, {'date': datetime.date(2007, 12, 7)}, {'date': datetime.date(2008, 3, 4)}, {'date': datetime.date(2013, 9, 16)}, {'date': datetime.date(2009, 11, 22)}, {'date': datetime.date(2014, 9, 19)}, {'date': datetime.date(2008, 10, 20)}, {'date': datetime.date(2010, 12, 16)}, {'date': datetime.date(2013, 3, 15)}, {'date': datetime.date(2012, 4, 25)}, {'date': datetime.date(2009, 5, 10)}]

for x in range(len(T.edges())) :
    a, b = T.edges()[x]
    T.edge[a][b]["date"] = edge_meta[x]["date"]



# Define nodes_with_m_nbrs()
def nodes_with_m_nbrs(G, m):
    """
    Returns all nodes in graph G that have m neighbors.
    """
    nodes = set()
    
    # Iterate over all nodes in G
    for n in G.nodes():
        # Check if the number of neighbors of n matches m
        if len(G.neighbors(n)) == m:
            # Add the node n to the set
            nodes.add(n)
    # Return the nodes with m neighbors
    return nodes

# Compute and print all nodes in T that have 3 neighbors
three_nbrs = nodes_with_m_nbrs(T, 3)
print(three_nbrs)


# Compute the degree of every node: degrees
degrees = [len(T.neighbors(n)) for n in T.nodes()]

# Print the degrees
print(degrees)


# Compute the degree centrality of the Twitter network: deg_cent
deg_cent = nx.degree_centrality(T)

# Plot a histogram of the degree centrality distribution of the graph.
plt.figure()
plt.hist(list(deg_cent.values()))
# plt.show()
plt.savefig("_dummyPy029.png", bbox_inches="tight")
plt.clf()

# Plot a histogram of the degree distribution of the graph
plt.figure()
plt.hist(degrees)
# plt.show()
plt.savefig("_dummyPy030.png", bbox_inches="tight")
plt.clf()

# Plot a scatter plot of the centrality distribution and the degree distribution
plt.figure()
plt.scatter(degrees, list(deg_cent.values()))
# plt.show()
plt.savefig("_dummyPy031.png", bbox_inches="tight")
plt.clf()


def path_exists(G, node1, node2):
    """
    This function checks whether a path exists between two nodes (node1, node2) in graph G.
    """
    visited_nodes = set()
    queue = [node1]
    
    for node in queue:  
        neighbors = G.neighbors(node)
        if node2 in neighbors:
            print('Path exists between nodes {0} and {1}'.format(node1, node2))
            return True
            break
            
        else:
            visited_nodes.add(node)
            queue.extend([n for n in neighbors if n not in visited_nodes])
            
        # Check to see if the final element of the queue has been reached
        if node == queue[-1]:
            print('Path does not exist between nodes {0} and {1}'.format(node1, node2))
            
            # Place the appropriate return statement
            return False


# Compute the betweenness centrality of T: bet_cen
bet_cen = nx.betweenness_centrality(T)

# Compute the degree centrality of T: deg_cen
deg_cen = nx.degree_centrality(T)

# Create a scatter plot of betweenness centrality and degree centrality
plt.scatter(list(bet_cen.values()), list(deg_cen.values()))

# Display the plot
# plt.show()
plt.savefig("_dummyPy032.png", bbox_inches="tight")
plt.clf()


# Define find_nodes_with_highest_deg_cent()
def find_nodes_with_highest_deg_cent(G):
    # Compute the degree centrality of G: deg_cent
    deg_cent = nx.degree_centrality(G)
    
    # Compute the maximum degree centrality: max_dc
    max_dc = max(list(deg_cent.values()))
    
    nodes = set()
    
    # Iterate over the degree centrality dictionary
    for k, v in deg_cent.items():
        # Check if the current value has the maximum degree centrality
        if v == max_dc:
            # Add the current node to the set of nodes
            nodes.add(k)
            
    return nodes
    
# Find the node(s) that has the highest degree centrality in T: top_dc
top_dc = find_nodes_with_highest_deg_cent(T)
print(top_dc)

# Write the assertion statement
for node in top_dc:
    assert nx.degree_centrality(T)[node] == max(nx.degree_centrality(T).values())


# Define find_node_with_highest_bet_cent()
def find_node_with_highest_bet_cent(G):
    # Compute betweenness centrality: bet_cent
    bet_cent = nx.betweenness_centrality(G)
    
    # Compute maximum betweenness centrality: max_bc
    max_bc = max(list(bet_cent.values()))
    
    nodes = set()
    
    # Iterate over the betweenness centrality dictionary
    for k, v in bet_cent.items():
        # Check if the current value has the maximum betweenness centrality
        if v == max_bc:
            # Add the current node to the set of nodes
            nodes.add(k)
            
    return nodes

# Use that function to find the node(s) that has the highest betweenness centrality in the network: top_bc
top_bc = find_node_with_highest_bet_cent(T)
print(top_bc)

# Write an assertion statement that checks that the node(s) is/are correctly identified.
for node in top_bc:
    assert nx.betweenness_centrality(T)[node] == max(nx.betweenness_centrality(T).values())

## {45, 37}
## [47, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4, 0, 4, 15, 0, 0, 0, 0, 0, 0, 0, 0, 22, 0, 0, 0, 0, 0, 0, 0, 4, 3, 0, 9, 0, 0, 1, 7, 0, 3, 0, 0, 0, 0]
## {1}
## {1}

Histogram of degree centrality:

Histogram of degree distribution:

Scatter plot of degree centrality vs degree distribution:

Scatter plot of degree centrality vs between centrality:

---

Chapter 3 - Structures

Cliques and communities - idea of tightly-knit groups:

• In network theory, a “clique” is a set of nodes that are full connected to each other by way of an edge
  
• Triangle closures are the idea that if A and B are connected and if A and C are connected, but that B and C are not connected, then connecting B and C will form a “clique”
  
• A helpful package “itertools” has a function “combinations” that can help to iterate over many combinations (rather than a double for loop)
  • For example, combinations(“ABC”, 2) will create (“A”, “B”), (“A”, “C”), (“B”, “C”)
    
  • This will be an iterable, but it will not print by itself

Maximal cliques - defined as a clique that when expanded by one node is no longer a clique:

• Basically, there is no single extension (one extra edge) that would make the clique larger
  
• Communities are an expansion of the idea of communities
  
• One possible definition of “communities” would be maximal cliques that are of at least size x and that have at least y members in common
  
• The find_cliques() function will find all of the maximal cliques in the network data

Sub-graphs - sometimes helpful to view just a small portion of a larger graph:

• Can use commands such as Gnew = G.subgraph(myNodes) # will just contain the nodes of interest, as well as their edges to each other
  
• Can then look at nx.draw(Gnew, with_labels=True) to request that labels be included on the visual

Example code includes:

from itertools import combinations

# Define is_in_triangle() 
def is_in_triangle(G, n):
    """
    Checks whether a node `n` in graph `G` is in a triangle relationship or not. 
    Returns a boolean.
    """
    in_triangle = False
    
    # Iterate over all possible triangle relationship combinations
    for n1, n2 in combinations(G.neighbors(n), 2):
        # Check if an edge exists between n1 and n2
        if G.has_edge(n1, n2):
            in_triangle = True
            break
    return in_triangle

# DO NOT HAVE T (make randomly, minus metadata)
import networkx as nx
import random
import numpy as np
import matplotlib.pyplot as plt

T = nx.Graph()
T.add_nodes_from([x for x in range(1, 31)])
np.random.seed(170530)
n1 = np.random.choice(range(1, 31), size=100, replace=True)
n2 = np.random.choice(range(1, 31), size=100, replace=True)

# Require that first be less than second
edge_list = [(min(x, y), max(x, y)) for x, y in zip(n1, n2) if x != y]
T.add_edges_from(edge_list)


# APPEARS THAT the set() makes sure to keep a sorted and unique list; if a = set(1, 2) and a.add(1) is run, than a will still be {1, 2}
# Can remove items from the set using a.remove() and can add items to the set using a.add()

# Write a function that identifies all nodes in a triangle relationship with a given node.
def nodes_in_triangle(G, n):
    """
    Returns the nodes in a graph `G` that are involved in a triangle relationship with the node `n`.
    """
    triangle_nodes = set([n])
    
    # Iterate over all possible triangle relationship combinations
    for n1, n2 in combinations(G.neighbors(n), 2):
        # Check if n1 and n2 have an edge between them
        if G.has_edge(n1, n2):
            # Add n1 to triangle_nodes
            triangle_nodes.add(n1)
            
            # Add n2 to triangle_nodes
            triangle_nodes.add(n2)
            
    return triangle_nodes
    
# Write the assertion statement
assert len(nodes_in_triangle(T, 1)) == 5  # happens to be what the RNG generated in this case


# Define node_in_open_triangle()
def node_in_open_triangle(G, n):
    """
    Checks whether pairs of neighbors of node `n` in graph `G` are in an 'open triangle' relationship with node `n`.
    """
    in_open_triangle = False
    
    # Iterate over all possible triangle relationship combinations
    for n1, n2 in combinations(G.neighbors(n), 2):
        # Check if n1 and n2 do NOT have an edge between them
        if not G.has_edge(n1, n2):
            in_open_triangle = True
            break
            
    return in_open_triangle

# Compute the number of open triangles in T
num_open_triangles = 0

# Iterate over all the nodes in T
for n in T.nodes():
    
    # Check if the current node is in an open triangle
    if node_in_open_triangle(T, n):
        
        # Increment num_open_triangles
        num_open_triangles += 1
    

print(num_open_triangles)


# Define maximal_cliques()
def maximal_cliques(G, size):
    """
    Finds all maximal cliques in graph `G` that are of size `size`.
    """
    mcs = []
    for clique in nx.find_cliques(G):
        if len(clique) == size:
            mcs.append(clique)
    return mcs

# Check that there are 33 maximal cliques of size 3 in the graph T
assert len(maximal_cliques(T, 3)) == 26  # happens to be what the RNG returns in this case


# Define get_nodes_and_nbrs()
def get_nodes_and_nbrs(G, nodes_of_interest):
    """
    Returns a subgraph of the graph `G` with only the `nodes_of_interest` and their neighbors.
    """
    nodes_to_draw = []
    
    # Iterate over the nodes of interest
    for n in nodes_of_interest:
        # Append the nodes of interest to nodes_to_draw
        nodes_to_draw.append(n)
        
        # Iterate over all the neighbors of node n
        for nbr in G.neighbors(n):
            # Append the neighbors of n to nodes_to_draw
            nodes_to_draw.append(nbr)
        
    return G.subgraph(nodes_to_draw)


# Extract the subgraph with the nodes of interest: T_draw
nodes_of_interest = [8, 24, 26]
T_draw = get_nodes_and_nbrs(T, nodes_of_interest)

# Draw the subgraph to the screen
nx.draw(T_draw, with_labels=True)
# plt.show()
plt.savefig("_dummyPy033.png", bbox_inches="tight")


# Extract the nodes of interest: nodes
node_meta = [{'occupation': 'scientist', 'category': 'I'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'scientist', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'scientist', 'category': 'P'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'politician', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'scientist', 'category': 'D'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'celebrity', 'category': 'D'}, {'occupation': 'politician', 'category': 'D'}, {'occupation': 'politician', 'category': 'P'}, {'occupation': 'celebrity', 'category': 'I'}, {'occupation': 'celebrity', 'category': 'P'}, {'occupation': 'scientist', 'category': 'I'}, {'occupation': 'scientist', 'category': 'P'}]

for x in range(len(T.nodes())) :
    T.node[T.nodes()[x]]["occupation"] = node_meta[x]["occupation"]
    T.node[T.nodes()[x]]["category"] = node_meta[x]["category"]


nodes = [n for n, d in T.nodes(data=True) if d['occupation'] == 'celebrity']

# Create the set of nodes: nodeset
nodeset = set(nodes)

# Iterate over nodes
for n in nodeset:
    
    # Compute the neighbors of n: nbrs
    nbrs = T.neighbors(n)
    
    # Compute the union of nodeset and nbrs: nodeset
    nodeset = nodeset.union(nbrs)


# Compute the subgraph using nodeset: T_sub
T_sub = T.subgraph(nodeset)

# Draw T_sub to the screen
nx.draw(T_sub, with_labels=True)
# plt.show()
plt.savefig("_dummyPy034.png", bbox_inches="tight")

## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\networkx\drawing\nx_pylab.py:126: MatplotlibDeprecationWarning: pyplot.hold is deprecated.
##     Future behavior will be consistent with the long-time default:
##     plot commands add elements without first clearing the
##     Axes and/or Figure.
##   b = plt.ishold()
## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\networkx\drawing\nx_pylab.py:138: MatplotlibDeprecationWarning: pyplot.hold is deprecated.
##     Future behavior will be consistent with the long-time default:
##     plot commands add elements without first clearing the
##     Axes and/or Figure.
##   plt.hold(b)
## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\matplotlib\__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.
##   warnings.warn(self.msg_depr_set % key)
## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\matplotlib\rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0
##   warnings.warn("axes.hold is deprecated, will be removed in 3.0")
## 30

Example Sub-graph (anything touching any of [8, 24, 26]:

Example Sub-graph (specified “occupation” in metadata):

---

Chapter 4 - Case Study

Case study introduction - GitHub collaborator data:

• The data will be a GitHub use collaboration network
  
• The nodes will be the users and the edges will reflect collaboration on 1+ GitHub repositories
  
• Goals include 1) analyze structure, 2) visualize graph, and 3) build simple recommendation systems

Case Study Part II - Visualization using the nxviz API:

• circ = nv.CircosPlot(G) ; circ.draw() # Create the Circos plot
  
• Additionally, will use the “connected component subgraph” features of networkx
  
• A connected component subragph is defined as a group of nodes connected to each other (perhaps not as a clique; may be through hubs) but with no connection to some other group of nodes
  • nx.connected_component_subgraph(G) # forms a generator object; cast as list to read them

Case Study Part III: Cliques:

• Simplest clique is an edge
  
• Simplest complex clique is a triangle
  
• Maximal clique is a clique that cannot be extended just be adding one additional node
  
• The nx.find_cliques(G) will find all of the maximal cliques in G

Case Study Part IV: Additional Tasks (building a recommender):

• Find important users (share with the most other users - degree_centrality)
  
• Find largest communities of collaborators (maximal cliques)
  
• Build a collaboration recommendation system (open triangles)

Example code includes:

# Import necessary modules
import matplotlib.pyplot as plt
import networkx as nx 
import numpy as np
import random


# DO NOT HAVE Github collaborator graph "G"
# Dummy up the data - 20 each of 2 "flavors"
G = nx.Graph()
G.add_nodes_from([x for x in range(1, 41)])
np.random.seed(170531)

# Add edges for 1-20 with preference that they match to themselves
n1 = np.random.choice(range(1, 21), size=100, replace=True)
n2 = np.random.choice(range(1, 21), size=90, replace=True)
n3 = np.random.choice(range(21, 41), size=10, replace=True)

# Require that first be less than second
edge_list = [(min(x, y), max(x, y)) for x, y in zip(n1, np.append(n2, n3)) if x != y]
G.add_edges_from(edge_list)


# Add edges for 21-40 with preference that they match to themselves
n1 = np.random.choice(range(21, 41), size=50, replace=True)
n2 = np.random.choice(range(21, 41), size=40, replace=True)
n3 = np.random.choice(range(1, 21), size=10, replace=True)

# Require that first be less than second
edge_list = [(min(x, y), max(x, y)) for x, y in zip(n1, np.append(n2, n3)) if x != y]
G.add_edges_from(edge_list)

# Create two groupings for the nodes
node_meta = [{'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type01'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}, {'grouping': 'type02'}]

for x in range(len(G.nodes())) :
    G.node[G.nodes()[x]]["grouping"] = node_meta[x]["grouping"]



# Plot the degree distribution of the GitHub collaboration network
plt.hist(list(nx.degree_centrality(G).values()))
# plt.show()
plt.savefig("_dummyPy035.png", bbox_inches="tight")
plt.clf()



# Plot the degree distribution of the GitHub collaboration network
plt.hist(list(nx.betweenness_centrality(G).values()))
# plt.show()
plt.savefig("_dummyPy036.png", bbox_inches="tight")
plt.clf()


# Import necessary modules
from nxviz import MatrixPlot


# Calculate the largest connected component subgraph: largest_ccs
largest_ccs = sorted(nx.connected_component_subgraphs(G), key=lambda x: len(x))[-1]

# Create the customized MatrixPlot object: h
h = MatrixPlot(largest_ccs, node_grouping="grouping")

# Draw the MatrixPlot to the screen
h.draw()
# plt.show()
plt.savefig("_dummyPy037.png", bbox_inches="tight")


# Import necessary modules
from nxviz.plots import ArcPlot


# Iterate over all the nodes in G, including the metadata
for n, d in G.nodes(data=True):
    
    # Calculate the degree of each node: G.node[n]['degree']
    G.node[n]['degree'] = nx.degree(G, n)
    
# Create the ArcPlot object: a
a = ArcPlot(G, node_order="degree")

# Draw the ArcPlot to the screen
a.draw()
# plt.show()
plt.savefig("_dummyPy038.png", bbox_inches="tight")


# Import necessary modules
from nxviz import CircosPlot
 
 
# Iterate over all the nodes, including the metadata
for n, d in G.nodes(data=True):
    
    # Calculate the degree of each node: G.node[n]['degree']
    G.node[n]['degree'] = nx.degree(G, n)

# Create the CircosPlot object: c
c = CircosPlot(G, node_order="degree", node_grouping="grouping", node_color="grouping")

# Draw the CircosPlot object to the screen
c.draw()
# plt.show()
plt.savefig("_dummyPy039.png", bbox_inches="tight")


# Calculate the maximal cliques in G: cliques
cliques = nx.find_cliques(G)

# Count and print the number of maximal cliques in G
print(len(list(cliques)))


# Find the author(s) that are part of the largest maximal clique: largest_clique
largest_clique = sorted(nx.find_cliques(G), key=lambda x:len(x))[-1]

# Create the subgraph of the largest_clique: G_lc
G_lc = G.subgraph(largest_clique)

# Create the CircosPlot object: c
c = CircosPlot(G_lc)

# Draw the CircosPlot to the screen
c.draw()
# plt.show()
plt.savefig("_dummyPy040.png", bbox_inches="tight")


# Compute the degree centralities of G: deg_cent
deg_cent = nx.degree_centrality(G)

# Compute the maximum degree centrality: max_dc
max_dc = max(deg_cent.values())

# Find the user(s) that have collaborated the most: prolific_collaborators
prolific_collaborators = [n for n, dc in deg_cent.items() if dc == max_dc]

# Print the most prolific collaborator(s)
print(prolific_collaborators)


# Identify the largest maximal clique: largest_max_clique
largest_max_clique = set(sorted(nx.find_cliques(G), key=lambda x: len(x))[-1])

# Create a subgraph from the largest_max_clique: G_lmc
G_lmc = G.subgraph(largest_max_clique)

# Go out 1 degree of separation
for node in G_lmc.nodes():
    G_lmc.add_nodes_from(G.neighbors(node))
    G_lmc.add_edges_from(zip([node]*len(G.neighbors(node)), G.neighbors(node)))

# Record each node's degree centrality score
for n in G_lmc.nodes():
    G_lmc.node[n]['degree centrality'] = nx.degree_centrality(G_lmc)[n]
        
# Create the ArcPlot object: a
a = ArcPlot(G_lmc, node_order = "degree centrality")

# Draw the ArcPlot to the screen
a.draw()
# plt.show()
plt.savefig("_dummyPy041.png", bbox_inches="tight")


# Import necessary modules
from itertools import combinations
from collections import defaultdict

# Initialize the defaultdict: recommended
recommended = defaultdict(int)

# Iterate over all the nodes in G
for n, d in G.nodes(data=True):
    
    # Iterate over all possible triangle relationship combinations
    for n1, n2 in combinations(G.neighbors(n), 2):
        
        # Check whether n1 and n2 do not have an edge
        if not G.has_edge(n1, n2):
            
            # Increment recommended
            recommended[(n1, n2)] += 1


# Identify the top 10 pairs of users
all_counts = sorted(recommended.values())
top10_pairs = [pair for pair, count in recommended.items() if count > all_counts[-10]]
print(top10_pairs)

## 75
## [6]
## [(3, 5), (6, 8), (18, 1), (6, 2)]

Case study - degree distribution:

Case study - betweenness centrality:

Case study - MatrixPlot:

Case study - ArcPlot:

Case study - CircosPlot:

Case Study - CircosPlot (for largest clique):

Case Study - ArcPlot (ordered by degree centrality):

Python Import and Clean Data

Importing Data in Python (Part I)

Chapter 1 - Introduction and flat files

Welcome to the course - importing from 1) flat files, 2) other native data, and 3) relational databases:

• Begin by looking at text files - plain text and table data (each row is an observation)
  
• The python “open()” function is the easiest way to look at a file
  • filename = “myFile” ; fPointer = open(filename, mode=“r”), fText = fPointer.read(); file.close()
    
  • print(fText) # All the text will be printed to the console
    
• Alternately, can use “with open(”myFile“, mode=”r“) as fPointer: # the file will close when the with ends
  • The “with” statement is known as a context manager
    
  • The use of a context manager is a best practice, since you never have to worry about closing a file

The importance of flat files in data science:

• Flat files are text files containing records (which is to say “table data” with each row being an observation and each column being an attribute)
  
• Flat files may also have a header describing the columns of the data (important to know for the data import process)
  
• Flat files are especially relevant for data science since they are a nice way to store tidy data
  
• Flat files may be separated by delimitors (comma, tab, etc.)
  
• Imports may be done through numpy or pandas

Importing flat files using numpy (only for data that is purely numerical):

• numpy arrays are the Python standard for storing numerical data; efficient, fast, and clean, and also often essential for other packages
  
• numpy.loadtxt() - import numpy as np; myData=np.loadtxt(“myFile”, delimiter=, skiprows=0, usecols=myList, dtype=) # default delimiter is any whitespace, default skip-rows is 0, default usecols is ALL, dtype=str will load as strings
  • Tends to break down when loading mixed data types; these are typically better for pandas
    
• numpy.genfromtxt() is another option, though only briefly mentioned in this course

Importing flat files using pandas - create 2-D data structures with columns of different data types:

• The pandas package is designed to help elevate Python from data munging (where it has always been excellent) to the full data analysis workflow (which might otherwise require R)
  
• The pandas DataFrame is modeled off the data frame in R; same idea of observations (rows) and variables (columns)
  
• The pandas package is the current best practice for loading data from flat files in to Python
  
• In the most basic usage, myData = pd.read_csv(“myFile”) # assumes import pandas as pd called previously
  • myData.head() # shows the first 5 rows of the data
    
  • myData.values # This will be the associated numpy array

Example code includes:

# put in directory ./PythonInputFiles/
# moby_dick.txt (converted to romeo-full.txt)
# digits.csv (using mnist_test.csv)
# digits_header.txt (skipped)
# seaslug.txt (downloaded)
# titanic.csv (converted from R)
# titanic_corrupt.txt (skipped)

myPath = "./PythonInputFiles/"


# NEED FILE "moby_dick.txt" (used "romeo-full.txt" instead)
# Open a file: file
file = open(myPath + "romeo-full.txt", mode="r")

# Print it
print(file.read())

# Check whether file is closed
print(file.closed)

# Close file
file.close()

# Check whether file is closed
print(file.closed)


# Read & print the first 3 lines
with open(myPath + "romeo-full.txt") as file:
    print(file.readline())
    print(file.readline())
    print(file.readline())


# NEED DIGIT RECOGNITION SITE - see http://yann.lecun.com/exdb/mnist/
# Import package
import numpy as np

# Assign filename to variable: file
file = myPath + 'mnist_test.csv'

# Load file as array: digits
digits = np.loadtxt(file, delimiter=",")

# Print datatype of digits
print(type(digits))

# Select and reshape a row
im = digits[21, 1:]
im_sq = np.reshape(im, (28, 28))


import matplotlib.pyplot as plt  # so the plotting below can be done

# Plot reshaped data (matplotlib.pyplot already loaded as plt)
plt.imshow(im_sq, cmap='Greys', interpolation='nearest')
# plt.show()
plt.savefig("_dummyPy042.png", bbox_inches="tight")
plt.clf()

# File should be tab-delimited and with a header row (for the skiprows=1)
# Assign the filename: file
# file = 'digits_header.txt'

# Load the data: data
# data = np.loadtxt(file, delimiter="\t", skiprows=1, usecols=[0, 2])

# Print data
# print(data)


# NEED FILE FROM http://www.stat.ucla.edu/projects/datasets/seaslug-explanation.html
# Should be floats with a single text header row, and tab-delimited

# Assign filename: file
file = myPath + 'seaslug.txt'

# Import file: data
data = np.loadtxt(file, delimiter='\t', dtype=str)

# Print the first element of data
print(data[0])

# Import data as floats and skip the first row: data_float
data_float = np.loadtxt(file, delimiter="\t", dtype=float, skiprows=1)

# Print the 10th element of data_float
print(data_float[9])

# Plot a scatterplot of the data
plt.scatter(data_float[:, 0], data_float[:, 1])
plt.xlabel('time (min.)')
plt.ylabel('percentage of larvae')
# plt.show()
plt.savefig("_dummyPy043.png", bbox_inches="tight")
plt.clf()

# NEED FILE "titanic.csv"
# Idea is that np.genfromtxt() and np.recfromcsv() can accept mixed data types through making each row its own array; dtype=None lets Python pick the data type by column

# Assign the filename: file
# file = myPath + 'titanic.csv'

# Import file using np.recfromcsv: d
# d=np.recfromcsv(file)   # This is like np.genfromtxt() with defaults set to dtype=None, delimiter=",", names=True

# Print out first three entries of d
# print(d[:3])


# PassengerId-Survived-Pclass-Sex-Age-SibSp-Parch-Ticket-Fare-Cabin-Embarked
# Import pandas as pd
import pandas as pd

# Assign the filename: file
file = myPath + 'titanic.csv'

# Read the file into a DataFrame: df
df = pd.read_csv(file)

# View the head of the DataFrame
print(df.head())



# Assign the filename: file
file = myPath + 'mnist_test.csv'

# Read the first 5 rows of the file into a DataFrame: data
data=pd.read_csv(file, nrows=5, header=None)

# Build a numpy array from the DataFrame: data_array
data_array = data.values

# Print the datatype of data_array to the shell
print(type(data_array))


# Assign filename: file
# file = 'titanic_corrupt.txt'

# Import file: data
# data = pd.read_csv(file, sep="\t", comment="#", na_values=["Nothing"])

# Print the head of the DataFrame
# print(data.head())

# Plot 'Age' variable in a histogram
# pd.DataFrame.hist(data[['Age']])
# plt.xlabel('Age (years)')
# plt.ylabel('count')
# plt.show()

## Romeo and Juliet
## Act 2, Scene 2 
## 
## SCENE II. Capulet's orchard.
## 
## Enter ROMEO
## 
## ROMEO
## 
## He jests at scars that never felt a wound.
## JULIET appears above at a window
## 
## But, soft! what light through yonder window breaks?
## It is the east, and Juliet is the sun.
## Arise, fair sun, and kill the envious moon,
## Who is already sick and pale with grief,
## That thou her maid art far more fair than she:
## Be not her maid, since she is envious;
## Her vestal livery is but sick and green
## And none but fools do wear it; cast it off.
## It is my lady, O, it is my love!
## O, that she knew she were!
## She speaks yet she says nothing: what of that?
## Her eye discourses; I will answer it.
## I am too bold, 'tis not to me she speaks:
## Two of the fairest stars in all the heaven,
## Having some business, do entreat her eyes
## To twinkle in their spheres till they return.
## What if her eyes were there, they in her head?
## The brightness of her cheek would shame those stars,
## As daylight doth a lamp; her eyes in heaven
## Would through the airy region stream so bright
## That birds would sing and think it were not night.
## See, how she leans her cheek upon her hand!
## O, that I were a glove upon that hand,
## That I might touch that cheek!
## 
## JULIET
## 
## Ay me!
## 
## ROMEO
## 
## She speaks:
## O, speak again, bright angel! for thou art
## As glorious to this night, being o'er my head
## As is a winged messenger of heaven
## Unto the white-upturned wondering eyes
## Of mortals that fall back to gaze on him
## When he bestrides the lazy-pacing clouds
## And sails upon the bosom of the air.
## 
## JULIET
## 
## O Romeo, Romeo! wherefore art thou Romeo?
## Deny thy father and refuse thy name;
## Or, if thou wilt not, be but sworn my love,
## And I'll no longer be a Capulet.
## 
## ROMEO
## 
## [Aside] Shall I hear more, or shall I speak at this?
## 
## JULIET
## 
## 'Tis but thy name that is my enemy;
## Thou art thyself, though not a Montague.
## What's Montague? it is nor hand, nor foot,
## Nor arm, nor face, nor any other part
## Belonging to a man. O, be some other name!
## What's in a name? that which we call a rose
## By any other name would smell as sweet;
## So Romeo would, were he not Romeo call'd,
## Retain that dear perfection which he owes
## Without that title. Romeo, doff thy name,
## And for that name which is no part of thee
## Take all myself.
## 
## ROMEO
## 
## I take thee at thy word:
## Call me but love, and I'll be new baptized;
## Henceforth I never will be Romeo.
## 
## JULIET
## 
## What man art thou that thus bescreen'd in night
## So stumblest on my counsel?
## 
## ROMEO
## 
## By a name
## I know not how to tell thee who I am:
## My name, dear saint, is hateful to myself,
## Because it is an enemy to thee;
## Had I it written, I would tear the word.
## 
## JULIET
## 
## My ears have not yet drunk a hundred words
## Of that tongue's utterance, yet I know the sound:
## Art thou not Romeo and a Montague?
## 
## ROMEO
## 
## Neither, fair saint, if either thee dislike.
## 
## JULIET
## 
## How camest thou hither, tell me, and wherefore?
## The orchard walls are high and hard to climb,
## And the place death, considering who thou art,
## If any of my kinsmen find thee here.
## 
## ROMEO
## 
## With love's light wings did I o'er-perch these walls;
## For stony limits cannot hold love out,
## And what love can do that dares love attempt;
## Therefore thy kinsmen are no let to me.
## 
## JULIET
## 
## If they do see thee, they will murder thee.
## 
## ROMEO
## 
## Alack, there lies more peril in thine eye
## Than twenty of their swords: look thou but sweet,
## And I am proof against their enmity.
## 
## JULIET
## 
## I would not for the world they saw thee here.
## 
## ROMEO
## 
## I have night's cloak to hide me from their sight;
## And but thou love me, let them find me here:
## My life were better ended by their hate,
## Than death prorogued, wanting of thy love.
## 
## JULIET
## 
## By whose direction found'st thou out this place?
## 
## ROMEO
## 
## By love, who first did prompt me to inquire;
## He lent me counsel and I lent him eyes.
## I am no pilot; yet, wert thou as far
## As that vast shore wash'd with the farthest sea,
## I would adventure for such merchandise.
## 
## JULIET
## 
## Thou know'st the mask of night is on my face,
## Else would a maiden blush bepaint my cheek
## For that which thou hast heard me speak to-night
## Fain would I dwell on form, fain, fain deny
## What I have spoke: but farewell compliment!
## Dost thou love me? I know thou wilt say 'Ay,'
## And I will take thy word: yet if thou swear'st,
## Thou mayst prove false; at lovers' perjuries
## Then say, Jove laughs. O gentle Romeo,
## If thou dost love, pronounce it faithfully:
## Or if thou think'st I am too quickly won,
## I'll frown and be perverse an say thee nay,
## So thou wilt woo; but else, not for the world.
## In truth, fair Montague, I am too fond,
## And therefore thou mayst think my 'havior light:
## But trust me, gentleman, I'll prove more true
## Than those that have more cunning to be strange.
## I should have been more strange, I must confess,
## But that thou overheard'st, ere I was ware,
## My true love's passion: therefore pardon me,
## And not impute this yielding to light love,
## Which the dark night hath so discovered.
## 
## ROMEO
## 
## Lady, by yonder blessed moon I swear
## That tips with silver all these fruit-tree tops--
## 
## JULIET
## 
## O, swear not by the moon, the inconstant moon,
## That monthly changes in her circled orb,
## Lest that thy love prove likewise variable.
## 
## ROMEO
## 
## What shall I swear by?
## 
## JULIET
## 
## Do not swear at all;
## Or, if thou wilt, swear by thy gracious self,
## Which is the god of my idolatry,
## And I'll believe thee.
## 
## ROMEO
## 
## If my heart's dear love--
## 
## JULIET
## 
## Well, do not swear: although I joy in thee,
## I have no joy of this contract to-night:
## It is too rash, too unadvised, too sudden;
## Too like the lightning, which doth cease to be
## Ere one can say 'It lightens.' Sweet, good night!
## This bud of love, by summer's ripening breath,
## May prove a beauteous flower when next we meet.
## Good night, good night! as sweet repose and rest
## Come to thy heart as that within my breast!
## 
## ROMEO
## 
## O, wilt thou leave me so unsatisfied?
## 
## JULIET
## 
## What satisfaction canst thou have to-night?
## 
## ROMEO
## 
## The exchange of thy love's faithful vow for mine.
## 
## JULIET
## 
## I gave thee mine before thou didst request it:
## And yet I would it were to give again.
## 
## ROMEO
## 
## Wouldst thou withdraw it? for what purpose, love?
## 
## JULIET
## 
## But to be frank, and give it thee again.
## And yet I wish but for the thing I have:
## My bounty is as boundless as the sea,
## My love as deep; the more I give to thee,
## The more I have, for both are infinite.
## 
## Nurse calls within
## 
## I hear some noise within; dear love, adieu!
## Anon, good nurse! Sweet Montague, be true.
## Stay but a little, I will come again.
## Exit, above
## 
## ROMEO
## 
## O blessed, blessed night! I am afeard.
## Being in night, all this is but a dream,
## Too flattering-sweet to be substantial.
## 
## Re-enter JULIET, above
## 
## JULIET
## 
## Three words, dear Romeo, and good night indeed.
## If that thy bent of love be honourable,
## Thy purpose marriage, send me word to-morrow,
## By one that I'll procure to come to thee,
## Where and what time thou wilt perform the rite;
## And all my fortunes at thy foot I'll lay
## And follow thee my lord throughout the world.
## 
## Nurse
## 
## [Within] Madam!
## 
## JULIET
## 
## I come, anon.--But if thou mean'st not well,
## I do beseech thee--
## 
## Nurse
## [Within] Madam!
## 
## JULIET
## 
## By and by, I come:--
## To cease thy suit, and leave me to my grief:
## To-morrow will I send.
## 
## ROMEO
## 
## So thrive my soul--
## 
## JULIET
## 
## A thousand times good night!
## Exit, above
## 
## ROMEO
## 
## A thousand times the worse, to want thy light.
## Love goes toward love, as schoolboys from
## their books,
## But love from love, toward school with heavy looks.
## Retiring
## 
## Re-enter JULIET, above
## 
## JULIET
## 
## Hist! Romeo, hist! O, for a falconer's voice,
## To lure this tassel-gentle back again!
## Bondage is hoarse, and may not speak aloud;
## Else would I tear the cave where Echo lies,
## And make her airy tongue more hoarse than mine,
## With repetition of my Romeo's name.
## 
## ROMEO
## 
## It is my soul that calls upon my name:
## How silver-sweet sound lovers' tongues by night,
## Like softest music to attending ears!
## 
## JULIET
## 
## Romeo!
## 
## ROMEO
## 
## My dear?
## 
## JULIET
## 
## At what o'clock to-morrow
## Shall I send to thee?
## 
## ROMEO
## 
## At the hour of nine.
## 
## JULIET
## 
## I will not fail: 'tis twenty years till then.
## I have forgot why I did call thee back.
## 
## ROMEO
## 
## Let me stand here till thou remember it.
## 
## JULIET
## 
## I shall forget, to have thee still stand there,
## Remembering how I love thy company.
## 
## ROMEO
## 
## And I'll still stay, to have thee still forget,
## Forgetting any other home but this.
## 
## JULIET
## 
## 'Tis almost morning; I would have thee gone:
## And yet no further than a wanton's bird;
## Who lets it hop a little from her hand,
## Like a poor prisoner in his twisted gyves,
## And with a silk thread plucks it back again,
## So loving-jealous of his liberty.
## 
## ROMEO
## 
## I would I were thy bird.
## 
## JULIET
## 
## Sweet, so would I:
## Yet I should kill thee with much cherishing.
## Good night, good night! parting is such
## sweet sorrow,
## That I shall say good night till it be morrow.
## 
## Exit above
## 
## ROMEO
## 
## Sleep dwell upon thine eyes, peace in thy breast!
## Would I were sleep and peace, so sweet to rest!
## Hence will I to my ghostly father's cell,
## His help to crave, and my dear hap to tell.
## 
## Exit
## 
## False
## True
## Romeo and Juliet
## 
## Act 2, Scene 2 
## 
## 
## 
## <class 'numpy.ndarray'>
## ["b'Time'" "b'Percent'"]
## [ 0.     0.357]
##    Unnamed: 0  PassengerId  Survived  Pclass  \
## 0           1            1         0       3   
## 1           2            2         1       1   
## 2           3            3         1       3   
## 3           4            4         1       1   
## 4           5            5         0       3   
## 
##                                                 Name     Sex   Age  SibSp  \
## 0                            Braund, Mr. Owen Harris    male  22.0      1   
## 1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   
## 2                             Heikkinen, Miss. Laina  female  26.0      0   
## 3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   
## 4                           Allen, Mr. William Henry    male  35.0      0   
## 
##    Parch            Ticket     Fare Cabin Embarked  
## 0      0         A/5 21171   7.2500   NaN        S  
## 1      0          PC 17599  71.2833   C85        C  
## 2      0  STON/O2. 3101282   7.9250   NaN        S  
## 3      0            113803  53.1000  C123        S  
## 4      0            373450   8.0500   NaN        S  
## <class 'numpy.ndarray'>

Example Image Recognition Digit:

Sea Slug Data:

---

Chapter 2 - Importing data from other file types

Introduction to other files types - Excel spreadsheets, MATLAB, SAS, Stata, HDF5 (becoming a more relevant format for saving data):

• There are also “pickled” files which are native to Python; idea is that you can serialize files like dictionaries or lists for later use in Python (rather than using json which is more human-readable)
  
• Opening a pickled file: import pickle; with open(“myFile,pkl”, mode=“rb”) as file: data=pickle.load(file)
  
• Excel files can generally be opened using data=pd.ExcelFile(“myExcel.xlsx”) # assumes previous import pandas as pd; automatically loads the Excel sheet as a data frame
  • data.sheet_names # provides a list of the sheet names
    
  • df1 = data.parse(“sheetName”) # can pass either the index as a float or the sheet name as a string
    
  • Can also skip rows and import only certain columns

Importing SAS/Stata files using pandas:

• SAS: Statistical Analysis System is common for business analytics and biostatistics
  
• Stata: Statistics + Data is common for academic social sciences research
  
• The most common SAS files have the extensions .sas7bdat and .sas7cdat
  • from sas7bdat import SAS7BDAT
    
  • with SAS7BDAT(“mySASfile.sas7bdat”) as file: df_sas=file.to_data_frame() # as per previous examples
    
• The Stata files can be imported directly using pd
  • pd.read_stata(“myStataFile.dta”)

Importing HDF5 (Hierarchical Data Format 5) files, quickly becoming the Python standard for storing large quantities of numerical data:

• HDF5 can scale up to exabytes of data, and is commonly used for files of hundereds of gigabytes or even terabytes
  
• import h5py; data=h5py.File(“myHD5.hd5”, “r”); for key in data.keys(): print(key)
  • might have “meta”, “quality”, and “strain” for a specific LIGO data file
    
  • could dive further in to any of the keys, for example for key in data[“meta”].keys(): print(key)
    
• The HDF project is formally managed by the HDF group, a Champaign-based spinoff of the University of Illinois

Importing MATLAB (MATrix LABoratory) files - industry standard in engineering and science:

• The library scipy has functions scipy.io.loadmat() and scipy.io.savemat()
  • The loaded file will be a dictionary (keys are the variable names and values are the objects assigned to the variables)
    
• COULD NOT GET scipy to import (lack of blas???)

Example code includes:

myPath = "./PythonInputFiles/"

# Import pickle package
import pickle

# NEED PICKLE DATA - {'Mar': '84.4', 'June': '69.4', 'Airline': '8', 'Aug': '85'}
# Created using with open(myPath + "data.pkl", "wb") as file: pickle.dump(myDict, file)
# Open pickle file and load data: d
with open(myPath + 'data.pkl', mode="rb") as file:
    d = pickle.load(file)

# Print d
print(d)

# Print datatype of d
print(type(d))


# NEED BATTLE DEATHS DATA - https://www.prio.org/Data/Armed-Conflict/Battle-Deaths/The-Battle-Deaths-Dataset-version-30/ (downloaded and converted name to "battledeath.xlsx")
# Import pandas
import pandas as pd

# Assign spreadsheet filename: file
file = myPath + "battledeath.xlsx"

# Load spreadsheet: xl
xl = pd.ExcelFile(file)

# Print sheet names
print(xl.sheet_names)


# Load a sheet into a DataFrame by name: df1
# There is only one sheet absent converting "bdonly" to a file by year
df1 = xl.parse("bdonly")

# Print the head of the DataFrame df1
print(df1.head())

# Load a sheet into a DataFrame by index: df2
df2 = xl.parse(0)

# Print the head of the DataFrame df2
print(df2.head())


# Parse the first sheet and rename the columns: df1
df1 = xl.parse(0, skiprows=[0], parse_cols=[2, 9], names=["AAM due to War (2002)", "Country"])

# Print the head of the DataFrame df1
print(df1.head())

# Parse the tenth column of the first sheet and rename the column: df2
df2 = xl.parse(0, parse_cols=[9], skiprows=[0], names=["Country"])

# Print the head of the DataFrame df2
print(df2.head())


# DO NOT HAVE THIS FILE EITHER
# Import sas7bdat package
from sas7bdat import SAS7BDAT

# Save file to a DataFrame: df_sas
# with SAS7BDAT('sales.sas7bdat') as file:
#     df_sas = file.to_data_frame()

# Print head of DataFrame
# print(df_sas.head())

import matplotlib.pyplot as plt

# Plot histogram of DataFrame features (pandas and pyplot already imported)
# pd.DataFrame.hist(df_sas[['P']])
# plt.ylabel('count')
# plt.show()


# DO NOT HAVE THIS FILE EITHER
# Import pandas

# Load Stata file into a pandas DataFrame: df
# df = pd.read_stata("disarea.dta")

# Print the head of the DataFrame df
# print(df.head())

# Plot histogram of one column of the DataFrame
# pd.DataFrame.hist(df[['disa10']])
# plt.xlabel('Extent of disease')
# plt.ylabel('Number of coutries')
# plt.show()


# DO NOT HAVE THIS FILE EITHER
# Import packages
import numpy as np
import h5py

# Assign filename: file
# file = 'LIGO_data.hdf5'

# Load file: data
# data = h5py.File(file, "r")

# Print the datatype of the loaded file
# print(type(data))

# Print the keys of the file
# for key in data.keys():
#     print(key)


# Get the HDF5 group: group
# group = data["strain"]

# Check out keys of group
# for key in group.keys():
#     print(key)

# Set variable equal to time series data: strain
# strain = data['strain']['Strain'].value

# Set number of time points to sample: num_samples
# num_samples = 10000

# Set time vector
# time = np.arange(0, 1, 1/num_samples)

# Plot data
# plt.plot(time, strain[:num_samples])
# plt.xlabel('GPS Time (s)')
# plt.ylabel('strain')
# plt.show()


# DO NOT HAVE THIS FILE EITHER - see https://www.mcb.ucdavis.edu/faculty-labs/albeck/workshop.htm
# Import package (cannot get to download)
# import scipy.io

# Load MATLAB file: mat
# mat = scipy.io.loadmat('albeck_gene_expression.mat')

# Print the datatype type of mat
# print(type(mat))


# Print the keys of the MATLAB dictionary
# print(mat.keys())

# Print the type of the value corresponding to the key 'CYratioCyt'
# print(type(mat["CYratioCyt"]))

# Print the shape of the value corresponding to the key 'CYratioCyt'
# print(np.shape(mat["CYratioCyt"]))

# Subset the array and plot it
# data = mat['CYratioCyt'][25, 5:]
# fig = plt.figure()
# plt.plot(data)
# plt.xlabel('time (min.)')
# plt.ylabel('normalized fluorescence (measure of expression)')
# plt.show()

## {'Mar': '84.4', 'June': '69.4', 'Airline': '8', 'Aug': '85'}
## <class 'dict'>
## ['bdonly']
##    id  year  bdeadlow  bdeadhig  bdeadbes  annualdata  source  bdversion  \
## 0   1  1946      1000      9999      1000           2       1          3   
## 1   1  1952       450      3000      -999           2       1          3   
## 2   1  1967        25       999        82           2       1          3   
## 3   2  1946        25       999      -999           0       0          3   
## 4   2  1947        25       999      -999           0       0          3   
## 
##    location    sidea   ...    epend  ependdate ependprec  gwnoa gwnoa2nd  \
## 0   Bolivia  Bolivia   ...        1 1946-07-21     -99.0    145      NaN   
## 1   Bolivia  Bolivia   ...        1 1952-04-12     -99.0    145      NaN   
## 2   Bolivia  Bolivia   ...        1 1967-10-16     -99.0    145      NaN   
## 3  Cambodia   France   ...        0        NaT       NaN    220      NaN   
## 4  Cambodia   France   ...        0        NaT       NaN    220      NaN   
## 
##    gwnob  gwnob2nd  gwnoloc region  version  
## 0    NaN       NaN      145      5   2009-4  
## 1    NaN       NaN      145      5   2009-4  
## 2    NaN       NaN      145      5   2009-4  
## 3    NaN       NaN      811      3   2009-4  
## 4    NaN       NaN      811      3   2009-4  
## 
## [5 rows x 32 columns]
##    id  year  bdeadlow  bdeadhig  bdeadbes  annualdata  source  bdversion  \
## 0   1  1946      1000      9999      1000           2       1          3   
## 1   1  1952       450      3000      -999           2       1          3   
## 2   1  1967        25       999        82           2       1          3   
## 3   2  1946        25       999      -999           0       0          3   
## 4   2  1947        25       999      -999           0       0          3   
## 
##    location    sidea   ...    epend  ependdate ependprec  gwnoa gwnoa2nd  \
## 0   Bolivia  Bolivia   ...        1 1946-07-21     -99.0    145      NaN   
## 1   Bolivia  Bolivia   ...        1 1952-04-12     -99.0    145      NaN   
## 2   Bolivia  Bolivia   ...        1 1967-10-16     -99.0    145      NaN   
## 3  Cambodia   France   ...        0        NaT       NaN    220      NaN   
## 4  Cambodia   France   ...        0        NaT       NaN    220      NaN   
## 
##    gwnob  gwnob2nd  gwnoloc region  version  
## 0    NaN       NaN      145      5   2009-4  
## 1    NaN       NaN      145      5   2009-4  
## 2    NaN       NaN      145      5   2009-4  
## 3    NaN       NaN      811      3   2009-4  
## 4    NaN       NaN      811      3   2009-4  
## 
## [5 rows x 32 columns]
##    AAM due to War (2002)  Country
## 0                    450  Bolivia
## 1                     25  Bolivia
## 2                     25   France
## 3                     25   France
## 4                     25   France
##    Country
## 0  Bolivia
## 1  Bolivia
## 2   France
## 3   France
## 4   France

---

Chapter 3 - Relational databases

Introduction to relational databases - standard discussion of how a relational database (system of tables) works:

• Each of the tables is a data frame, keyed by a primary key (unique identifier for the row in question)
  
• The tables are all linked by way of the primary keys, and the existence of these keys as columns in some of the other tables
  
• The relational linking process saves a great deal of space
  
• Many systems exist, such as PostgreSQL, MySQL, SQLite, and the like
  
• SQL is an acronym for “Structured Query Language” which is a standard way for interacting with the relational databases

Creating a database engine in Python - goal is to get data out of the relational database using SQL:

• SQLite is nice since it is fast and simple, though other databases may have additional valuable features
  
• The package “SQLAlchemy” works with many other RDBMS (relational database management systems)
  • from sqlalchemy import create_engine
    
  • engine = create_engine(“mySQLDatabase.sqlite”) # may have different extensions if a different type of database
    
  • engine.table_names() # provides the names of all the tables in engine

Querying relational databases in Python - connecting to the engine and then querying (getting data out from) the database:

• SELECT * FROM myTable will bring over all columns of all rows
  
• General workflow for SQL in Python include: 1) import packages, 2) create the DB engine, 3) connect to the engine, 4) query the database, 5) save query results to a DataFrame, and 6) close the connection
  • Step 3: con = engine.connect()
    
  • Step 4: rs = con.execute(“valid SQL queries”)
    
  • Step 5: df = pd.DataFrame(rs.fetchall()) ; df.columns = rs.keys() # if wanting to bring over meaningful column names
    
  • Step 6: con.close()
    
• A context manager (with engine.connect() as con) can save the hassle of con.close(), or worse forgetting to close the connection
  
• Note that rs.fetchmany(size=5) is an option for bringing over just 5 lines from the query (can use numbers other than 5 also)

Querying relational databases directly with pandas - shortcut to the above process:

• df = pd.read_sql_query(“valid SQL code”, engine) # where import pandas as pd and engine = create_engine(“mySQLConnection”) have previously been run

Advanced querying - exploiting table relationships (combining mutliple tables):

• The SQL join to bring 2+ tables together
  
• SELECT myVars FROM Table1 INNER JOIN Table2 ON joinCriteria
  • Note that the format for variables is Table.Variable, so Orders.CustomerID = Customers.CustomerID

Example code includes:

myPath = "./PythonInputFiles/"

# NEED FILE - may be able to get at http://chinookdatabase.codeplex.com/
# Downloaded the ZIP, extracted the SQLite, and renamed to Chinook.sqlite
# Import necessary module
from sqlalchemy import create_engine

# Create engine: engine
engine = create_engine('sqlite:///' + myPath + 'Chinook.sqlite')  # The sqlite:/// is called the 'connection string'


# Save the table names to a list: table_names
table_names = engine.table_names()

# Print the table names to the shell
print(table_names)


# Import packages
from sqlalchemy import create_engine
import pandas as pd

# Create engine: engine
engine = create_engine('sqlite:///' + myPath + 'Chinook.sqlite')

# Open engine connection: con
con = engine.connect()

# Perform query: rs
rs = con.execute("SELECT * FROM Album")

# Save results of the query to DataFrame: df
df = pd.DataFrame(rs.fetchall())

# Close connection
con.close()

# Print head of DataFrame df
print(df.head())


# Open engine in context manager
# Perform query and save results to DataFrame: df
with engine.connect() as con:
    rs = con.execute("SELECT LastName, Title FROM Employee")
    df = pd.DataFrame(rs.fetchmany(size=3))
    df.columns = rs.keys()

# Print the length of the DataFrame df
print(len(df))

# Print the head of the DataFrame df
print(df.head())


# Create engine: engine
engine = create_engine('sqlite:///' + myPath + 'Chinook.sqlite')

# Open engine in context manager
# Perform query and save results to DataFrame: df
with engine.connect() as con:
    rs = con.execute("SELECT * FROM Employee WHERE EmployeeID >= 6")
    df = pd.DataFrame(rs.fetchall())
    df.columns = rs.keys()

# Print the head of the DataFrame df
print(df.head())


# Create engine: engine
engine = create_engine('sqlite:///' + myPath + 'Chinook.sqlite')

# Open engine in context manager
with engine.connect() as con:
    rs = con.execute("SELECT * FROM Employee ORDER BY BirthDate")
    df = pd.DataFrame(rs.fetchall())
    
    # Set the DataFrame's column names
    df.columns = rs.keys()

# Print head of DataFrame
print(df.head())


# Import packages
from sqlalchemy import create_engine
import pandas as pd

# Create engine: engine
engine = create_engine('sqlite:///' + myPath + 'Chinook.sqlite')

# Execute query and store records in DataFrame: df
df = pd.read_sql_query("SELECT * FROM Album", engine)

# Print head of DataFrame
print(df.head())

# Open engine in context manager
# Perform query and save results to DataFrame: df1
with engine.connect() as con:
    rs = con.execute("SELECT * FROM Album")
    df1 = pd.DataFrame(rs.fetchall())
    df1.columns = rs.keys()

# Confirm that both methods yield the same result: does df = df1 ?
print(df.equals(df1))


# Import packages
from sqlalchemy import create_engine
import pandas as pd

# Create engine: engine
engine = create_engine('sqlite:///' + myPath + 'Chinook.sqlite')

# Execute query and store records in DataFrame: df
df = pd.read_sql_query("SELECT * FROM Employee WHERE EmployeeId >= 6 ORDER BY BirthDate", engine)

# Print head of DataFrame
print(df.head())


# Open engine in context manager
# Perform query and save results to DataFrame: df
with engine.connect() as con:
    rs = con.execute("SELECT Title, Name FROM Album INNER JOIN Artist ON Album.ArtistID = Artist.ArtistID")
    df = pd.DataFrame(rs.fetchall())
    df.columns = rs.keys()

# Print head of DataFrame df
print(df.head())


# Execute query and store records in DataFrame: df
df = pd.read_sql_query("SELECT * FROM PlaylistTrack INNER JOIN Track ON PlaylistTrack.TrackId = Track.TrackId WHERE Milliseconds < 250000", engine)

# Print head of DataFrame
print(df.head())

## ['Album', 'Artist', 'Customer', 'Employee', 'Genre', 'Invoice', 'InvoiceLine', 'MediaType', 'Playlist', 'PlaylistTrack', 'Track']
##    0                                      1  2
## 0  1  For Those About To Rock We Salute You  1
## 1  2                      Balls to the Wall  2
## 2  3                      Restless and Wild  2
## 3  4                      Let There Be Rock  1
## 4  5                               Big Ones  3
## 3
##   LastName                Title
## 0    Adams      General Manager
## 1  Edwards        Sales Manager
## 2  Peacock  Sales Support Agent
##    EmployeeId  LastName FirstName       Title  ReportsTo            BirthDate  \
## 0           6  Mitchell   Michael  IT Manager          1  1973-07-01 00:00:00   
## 1           7      King    Robert    IT Staff          6  1970-05-29 00:00:00   
## 2           8  Callahan     Laura    IT Staff          6  1968-01-09 00:00:00   
## 
##               HireDate                      Address        City State Country  \
## 0  2003-10-17 00:00:00         5827 Bowness Road NW     Calgary    AB  Canada   
## 1  2004-01-02 00:00:00  590 Columbia Boulevard West  Lethbridge    AB  Canada   
## 2  2004-03-04 00:00:00                  923 7 ST NW  Lethbridge    AB  Canada   
## 
##   PostalCode              Phone                Fax                    Email  
## 0    T3B 0C5  +1 (403) 246-9887  +1 (403) 246-9899  michael@chinookcorp.com  
## 1    T1K 5N8  +1 (403) 456-9986  +1 (403) 456-8485   robert@chinookcorp.com  
## 2    T1H 1Y8  +1 (403) 467-3351  +1 (403) 467-8772    laura@chinookcorp.com  
##    EmployeeId  LastName FirstName                Title  ReportsTo  \
## 0           4      Park  Margaret  Sales Support Agent        2.0   
## 1           2   Edwards     Nancy        Sales Manager        1.0   
## 2           1     Adams    Andrew      General Manager        NaN   
## 3           5   Johnson     Steve  Sales Support Agent        2.0   
## 4           8  Callahan     Laura             IT Staff        6.0   
## 
##              BirthDate             HireDate              Address        City  \
## 0  1947-09-19 00:00:00  2003-05-03 00:00:00     683 10 Street SW     Calgary   
## 1  1958-12-08 00:00:00  2002-05-01 00:00:00         825 8 Ave SW     Calgary   
## 2  1962-02-18 00:00:00  2002-08-14 00:00:00  11120 Jasper Ave NW    Edmonton   
## 3  1965-03-03 00:00:00  2003-10-17 00:00:00         7727B 41 Ave     Calgary   
## 4  1968-01-09 00:00:00  2004-03-04 00:00:00          923 7 ST NW  Lethbridge   
## 
##   State Country PostalCode              Phone                Fax  \
## 0    AB  Canada    T2P 5G3  +1 (403) 263-4423  +1 (403) 263-4289   
## 1    AB  Canada    T2P 2T3  +1 (403) 262-3443  +1 (403) 262-3322   
## 2    AB  Canada    T5K 2N1  +1 (780) 428-9482  +1 (780) 428-3457   
## 3    AB  Canada    T3B 1Y7   1 (780) 836-9987   1 (780) 836-9543   
## 4    AB  Canada    T1H 1Y8  +1 (403) 467-3351  +1 (403) 467-8772   
## 
##                       Email  
## 0  margaret@chinookcorp.com  
## 1     nancy@chinookcorp.com  
## 2    andrew@chinookcorp.com  
## 3     steve@chinookcorp.com  
## 4     laura@chinookcorp.com  
##    AlbumId                                  Title  ArtistId
## 0        1  For Those About To Rock We Salute You         1
## 1        2                      Balls to the Wall         2
## 2        3                      Restless and Wild         2
## 3        4                      Let There Be Rock         1
## 4        5                               Big Ones         3
## True
##    EmployeeId  LastName FirstName       Title  ReportsTo            BirthDate  \
## 0           8  Callahan     Laura    IT Staff          6  1968-01-09 00:00:00   
## 1           7      King    Robert    IT Staff          6  1970-05-29 00:00:00   
## 2           6  Mitchell   Michael  IT Manager          1  1973-07-01 00:00:00   
## 
##               HireDate                      Address        City State Country  \
## 0  2004-03-04 00:00:00                  923 7 ST NW  Lethbridge    AB  Canada   
## 1  2004-01-02 00:00:00  590 Columbia Boulevard West  Lethbridge    AB  Canada   
## 2  2003-10-17 00:00:00         5827 Bowness Road NW     Calgary    AB  Canada   
## 
##   PostalCode              Phone                Fax                    Email  
## 0    T1H 1Y8  +1 (403) 467-3351  +1 (403) 467-8772    laura@chinookcorp.com  
## 1    T1K 5N8  +1 (403) 456-9986  +1 (403) 456-8485   robert@chinookcorp.com  
## 2    T3B 0C5  +1 (403) 246-9887  +1 (403) 246-9899  michael@chinookcorp.com  
##                                    Title       Name
## 0  For Those About To Rock We Salute You      AC/DC
## 1                      Balls to the Wall     Accept
## 2                      Restless and Wild     Accept
## 3                      Let There Be Rock      AC/DC
## 4                               Big Ones  Aerosmith
##    PlaylistId  TrackId  TrackId              Name  AlbumId  MediaTypeId  \
## 0           1     3390     3390  One and the Same      271            2   
## 1           1     3392     3392     Until We Fall      271            2   
## 2           1     3393     3393     Original Fire      271            2   
## 3           1     3394     3394       Broken City      271            2   
## 4           1     3395     3395          Somedays      271            2   
## 
##    GenreId Composer  Milliseconds    Bytes  UnitPrice  
## 0       23     None        217732  3559040       0.99  
## 1       23     None        230758  3766605       0.99  
## 2       23     None        218916  3577821       0.99  
## 3       23     None        228366  3728955       0.99  
## 4       23     None        213831  3497176       0.99

Importing Data in Python (Part II)

Chapter 1 - Importing Data from the Internet

Importing flat files from the web - non-local files:

• Clicking on URL and downloading files creates reproducibility problems and is non-scalable
  
• Course covers 1) import and locally save from the web, 2) load datasets in to pandas DataFrames, 3) make HTTP requests, 4) scrape HTML (BeustifulSoup)
  
• This course will particularly focus on “urllib” and “requests” packages
  
• The “urllib” package has an interface for fetching data from across the web
  • urllib.urlopen(“myURL”) # Very similar to open() but takes an URL rather than a local file name
    
  • from urllib.request import urlretrieve ; url = “myQuotedURL” ; urlretrieve(url, “myLocalFileName”)

HTTP requests to import files from the web - unpacking the urlretrieve from urllib.request:

• URL is an acronym for Uniform/Universal Resource Locator (reference to web resources such as web addresses, FTP, and the like)
  
• Ingredients for an URL include 1) protocol identifier (such as “http:”) and a resource name (such as “datacamp.com”)
  
• HTTP is an acronym for Hyper-Text Transfer Protocol which is the foundation for data communication on the web
  • Going to a website is the process of sending a GET request through HTTP ; the urlretrieve does this automatically
    
• HTML is an acronym for HyperText Markup Language, which is the standard mark-up language used on the internet
  
• Example process for GET requests using urllib
  • from urllib.request import urlopen, Request
    
  • url = “https://www.wikipedia.org/” ; request = Request(url) ; response = urlopen(request) ; html = response.read() ; response.close()
    
• Can also send GET requests using “requests”“, a commonly used package that simplifies the process
  • import requests
    
  • url = “https://www.wikipedia.org/” ; r = requests.get(url) ; text = r.text

Scraping the web in Python using BeautifulSoup - make sense of the jumbled, unstructured HTML data:

• Structured data has either 1) a pre-defined data model, or 2) organization in a defined manner
  
• HTML is unstructured data, possessing neither of these properties
  
• BeautifulSoup parses and extracts structured data from HTML
  
• General usage would include
  • from bs4 import BeautifulSoup ; import requests
    
  • url = “https://www.crummy.com/software/BeautifulSoup/”
    
  • r = requests.get(url) ; html_doc = r.text
    
  • soup = BeautifulSoup(html_doc)
    
  • print(soup.prettify()) # printes properly indented html code, easier for human parsing

Example code includes:

# Import package
from urllib.request import urlretrieve
import pandas as pd

# Assign url of file: url
url = 'https://s3.amazonaws.com/assets.datacamp.com/production/course_1606/datasets/winequality-red.csv'

# Save file locally
urlretrieve(url, 'winequality-red.csv')

# Read file into a DataFrame and print its head
df = pd.read_csv('winequality-red.csv', sep=';')
print(df.head())


# Import packages
import matplotlib.pyplot as plt
import pandas as pd

# Assign url of file: url
url = 'https://s3.amazonaws.com/assets.datacamp.com/production/course_1606/datasets/winequality-red.csv'

# Read file into a DataFrame: df
df = pd.read_csv(url, sep=";")

# Print the head of the DataFrame
print(df.head())

# Plot first column of df
pd.DataFrame.hist(df.ix[:, 0:1])
plt.xlabel('fixed acidity (g(tartaric acid)/dm$^3$)')
plt.ylabel('count')
# plt.show()
plt.savefig("_dummyPy044.png", bbox_inches="tight")
plt.clf()


# Assign url of file: url
url = 'http://s3.amazonaws.com/assets.datacamp.com/course/importing_data_into_r/latitude.xls'

# Read in all sheets of Excel file: xl
xl = pd.read_excel(url, sheetname=None)

# Print the sheetnames to the shell
print(xl.keys())

# Print the head of the first sheet (using its name, NOT its index)
print(xl["1700"].head())


# Import packages
from urllib.request import urlopen, Request

# Specify the url
url = "http://www.datacamp.com/teach/documentation"

# This packages the request: request
request = Request(url)

# Sends the request and catches the response: response
response = urlopen(request)

# Print the datatype of response
print(type(response))

# Be polite and close the response!
response.close()


# Specify the url
url = "http://docs.datacamp.com/teach/"

# This packages the request
request = Request(url)

# Sends the request and catches the response: response
response = urlopen(request)

# Extract the response: html
html = response.read()

# Print the html
print(html)

# Be polite and close the response!
response.close()


import requests

# Specify the url: url
url = "http://docs.datacamp.com/teach/"

# Packages the request, send the request and catch the response: r
r = requests.get(url)

# Extract the response: text
text = r.text

# Print the html
print(text)


# Import packages
import requests
from bs4 import BeautifulSoup

# Specify url: url
url = 'https://www.python.org/~guido/'

# Package the request, send the request and catch the response: r
r = requests.get(url)

# Extracts the response as html: html_doc
html_doc = r.text

# Create a BeautifulSoup object from the HTML: soup
soup = BeautifulSoup(html_doc)

# Prettify the BeautifulSoup object: pretty_soup
pretty_soup = soup.prettify()

# Print the response
print(pretty_soup)


# Get the title of Guido's webpage: guido_title
guido_title = soup.title

# Print the title of Guido's webpage to the shell
print(guido_title)

# Get Guido's text: guido_text
guido_text = soup.get_text()

# Print Guido's text to the shell
print(guido_text)


# Find all 'a' tags (which define hyperlinks): a_tags
a_tags = soup.find_all("a")

# Print the URLs to the shell
for link in a_tags:
    print(link.get("href"))

##    fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \
## 0            7.4              0.70         0.00             1.9      0.076   
## 1            7.8              0.88         0.00             2.6      0.098   
## 2            7.8              0.76         0.04             2.3      0.092   
## 3           11.2              0.28         0.56             1.9      0.075   
## 4            7.4              0.70         0.00             1.9      0.076   
## 
##    free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \
## 0                 11.0                  34.0   0.9978  3.51       0.56   
## 1                 25.0                  67.0   0.9968  3.20       0.68   
## 2                 15.0                  54.0   0.9970  3.26       0.65   
## 3                 17.0                  60.0   0.9980  3.16       0.58   
## 4                 11.0                  34.0   0.9978  3.51       0.56   
## 
##    alcohol  quality  
## 0      9.4        5  
## 1      9.8        5  
## 2      9.8        5  
## 3      9.8        6  
## 4      9.4        5  
##    fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \
## 0            7.4              0.70         0.00             1.9      0.076   
## 1            7.8              0.88         0.00             2.6      0.098   
## 2            7.8              0.76         0.04             2.3      0.092   
## 3           11.2              0.28         0.56             1.9      0.075   
## 4            7.4              0.70         0.00             1.9      0.076   
## 
##    free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \
## 0                 11.0                  34.0   0.9978  3.51       0.56   
## 1                 25.0                  67.0   0.9968  3.20       0.68   
## 2                 15.0                  54.0   0.9970  3.26       0.65   
## 3                 17.0                  60.0   0.9980  3.16       0.58   
## 4                 11.0                  34.0   0.9978  3.51       0.56   
## 
##    alcohol  quality  
## 0      9.4        5  
## 1      9.8        5  
## 2      9.8        5  
## 3      9.8        6  
## 4      9.4        5  
## odict_keys(['1700', '1900'])
##                  country       1700
## 0            Afghanistan  34.565000
## 1  Akrotiri and Dhekelia  34.616667
## 2                Albania  41.312000
## 3                Algeria  36.720000
## 4         American Samoa -14.307000
## <class 'http.client.HTTPResponse'>
## b'<!DOCTYPE html>\n<link rel="shortcut icon" href="images/favicon.ico" />\n<html>\n\n  <head>\n  <meta charset="utf-8">\n  <meta http-equiv="X-UA-Compatible" content="IE=edge">\n  <meta name="viewport" content="width=device-width, initial-scale=1">\n\n  <title>Home</title>\n  <meta name="description" content="All Documentation on Course Creation">\n\n  <link rel="stylesheet" href="/teach/css/main.css">\n  <link rel="canonical" href="/teach/">\n  <link rel="alternate" type="application/rss+xml" title="DataCamp Teach Documentation" href="/teach/feed.xml" />\n</head>\n\n\n  <body>\n\n    <header class="site-header">\n\n  <div class="wrapper">\n\n    <a class="site-title" href="/teach/">DataCamp Teach Documentation</a>\n\n  </div>\n\n</header>\n\n\n    <div class="page-content">\n      <div class="wrapper">\n        <p>The Teach Documentation has been moved to <a href="https://www.datacamp.com/teach/documentation">https://www.datacamp.com/teach/documentation</a>!</p>\n\n<!-- Everybody can teach on DataCamp. The resources on this website explain all the steps to build your own course on DataCamp\'s interactive data science platform.\n\nInterested in partnering with DataCamp? Head over to the [Course Material](/teach/course-material.html) page to get an idea of the requirements to build your own interactive course together with DataCamp!\n\n## Table of Contents\n\n- [Course Material](/teach/course-material.html) - Content required to build a DataCamp course.\n- [Video Lectures](/teach/video-lectures.html) - Details on video recording and editing.\n- [DataCamp Teach](https://www.datacamp.com/teach) - Use the DataCamp Teach website to create DataCamp courses (preferred).\n- [datacamp R Package](https://github.com/datacamp/datacamp/wiki) - Use R Package to create DataCamp courses (legacy).\n- [Code DataCamp Exercises](/teach/code-datacamp-exercises.html)\n- [SCT Design (R)](https://github.com/datacamp/testwhat/wiki)\n- [SCT Design (Python)](https://github.com/datacamp/pythonwhat/wiki)\n- [Style Guide](/teach/style-guide.html) -->\n\n\n      </div>\n    </div>\n\n    \n\n  </body>\n\n</html>\n'
## <!DOCTYPE html>
## <link rel="shortcut icon" href="images/favicon.ico" />
## <html>
## 
##   <head>
##   <meta charset="utf-8">
##   <meta http-equiv="X-UA-Compatible" content="IE=edge">
##   <meta name="viewport" content="width=device-width, initial-scale=1">
## 
##   <title>Home</title>
##   <meta name="description" content="All Documentation on Course Creation">
## 
##   <link rel="stylesheet" href="/teach/css/main.css">
##   <link rel="canonical" href="/teach/">
##   <link rel="alternate" type="application/rss+xml" title="DataCamp Teach Documentation" href="/teach/feed.xml" />
## </head>
## 
## 
##   <body>
## 
##     <header class="site-header">
## 
##   <div class="wrapper">
## 
##     <a class="site-title" href="/teach/">DataCamp Teach Documentation</a>
## 
##   </div>
## 
## </header>
## 
## 
##     <div class="page-content">
##       <div class="wrapper">
##         <p>The Teach Documentation has been moved to <a href="https://www.datacamp.com/teach/documentation">https://www.datacamp.com/teach/documentation</a>!</p>
## 
## <!-- Everybody can teach on DataCamp. The resources on this website explain all the steps to build your own course on DataCamp's interactive data science platform.
## 
## Interested in partnering with DataCamp? Head over to the [Course Material](/teach/course-material.html) page to get an idea of the requirements to build your own interactive course together with DataCamp!
## 
## ## Table of Contents
## 
## - [Course Material](/teach/course-material.html) - Content required to build a DataCamp course.
## - [Video Lectures](/teach/video-lectures.html) - Details on video recording and editing.
## - [DataCamp Teach](https://www.datacamp.com/teach) - Use the DataCamp Teach website to create DataCamp courses (preferred).
## - [datacamp R Package](https://github.com/datacamp/datacamp/wiki) - Use R Package to create DataCamp courses (legacy).
## - [Code DataCamp Exercises](/teach/code-datacamp-exercises.html)
## - [SCT Design (R)](https://github.com/datacamp/testwhat/wiki)
## - [SCT Design (Python)](https://github.com/datacamp/pythonwhat/wiki)
## - [Style Guide](/teach/style-guide.html) -->
## 
## 
##       </div>
##     </div>
## 
##     
## 
##   </body>
## 
## </html>
## 
## <html>
##  <head>
##   <title>
##    Guido's Personal Home Page
##   </title>
##  </head>
##  <body bgcolor="#FFFFFF" text="#000000">
##   <h1>
##    <a href="pics.html">
##     <img border="0" src="images/IMG_2192.jpg"/>
##    </a>
##    Guido van Rossum - Personal Home Page
##   </h1>
##   <p>
##    <a href="http://www.washingtonpost.com/wp-srv/business/longterm/microsoft/stories/1998/raymond120398.htm">
##     <i>
##      "Gawky and proud of it."
##     </i>
##    </a>
##    <h3>
##     <a href="http://metalab.unc.edu/Dave/Dr-Fun/df200004/df20000406.jpg">
##      Who
## I Am
##     </a>
##    </h3>
##    <p>
##     Read
## my
##     <a href="http://neopythonic.blogspot.com/2016/04/kings-day-speech.html">
##      "King's
## Day Speech"
##     </a>
##     for some inspiration.
##     <p>
##      I am the author of the
##      <a href="http://www.python.org">
##       Python
##      </a>
##      programming language.  See also my
##      <a href="Resume.html">
##       resume
##      </a>
##      and my
##      <a href="Publications.html">
##       publications list
##      </a>
##      , a
##      <a href="bio.html">
##       brief bio
##      </a>
##      , assorted
##      <a href="http://legacy.python.org/doc/essays/">
##       writings
##      </a>
##      ,
##      <a href="http://legacy.python.org/doc/essays/ppt/">
##       presentations
##      </a>
##      and
##      <a href="interviews.html">
##       interviews
##      </a>
##      (all about Python), some
##      <a href="pics.html">
##       pictures of me
##      </a>
##      ,
##      <a href="http://neopythonic.blogspot.com">
##       my new blog
##      </a>
##      , and
## my
##      <a href="http://www.artima.com/weblogs/index.jsp?blogger=12088">
##       old
## blog
##      </a>
##      on Artima.com.  I am
##      <a href="https://twitter.com/gvanrossum">
##       @gvanrossum
##      </a>
##      on Twitter.  I
## also have
## a
##      <a href="https://plus.google.com/u/0/115212051037621986145/posts">
##       G+
## profile
##      </a>
##      .
##      <p>
##       In January 2013 I joined
##       <a href="http://www.dropbox.com">
##        Dropbox
##       </a>
##       .  I work on various Dropbox
## products and have 50% for my Python work, no strings attached.
## Previously, I have worked for Google, Elemental Security, Zope
## Corporation, BeOpen.com, CNRI, CWI, and SARA.  (See
## my
##       <a href="Resume.html">
##        resume
##       </a>
##       .)  I created Python while at CWI.
##       <h3>
##        How to Reach Me
##       </h3>
##       <p>
##        You can send email for me to guido (at) python.org.
## I read everything sent there, but if you ask
## me a question about using Python, it's likely that I won't have time
## to answer it, and will instead refer you to
## help (at) python.org,
##        <a href="http://groups.google.com/groups?q=comp.lang.python">
##         comp.lang.python
##        </a>
##        or
##        <a href="http://stackoverflow.com">
##         StackOverflow
##        </a>
##        .  If you need to
## talk to me on the phone or send me something by snail mail, send me an
## email and I'll gladly email you instructions on how to reach me.
##        <h3>
##         My Name
##        </h3>
##        <p>
##         My name often poses difficulties for Americans.
##         <p>
##          <b>
##           Pronunciation:
##          </b>
##          in Dutch, the "G" in Guido is a hard G,
## pronounced roughly like the "ch" in Scottish "loch".  (Listen to the
##          <a href="guido.au">
##           sound clip
##          </a>
##          .)  However, if you're
## American, you may also pronounce it as the Italian "Guido".  I'm not
## too worried about the associations with mob assassins that some people
## have. :-)
##          <p>
##           <b>
##            Spelling:
##           </b>
##           my last name is two words, and I'd like to keep it
## that way, the spelling on some of my credit cards notwithstanding.
## Dutch spelling rules dictate that when used in combination with my
## first name, "van" is not capitalized: "Guido van Rossum".  But when my
## last name is used alone to refer to me, it is capitalized, for
## example: "As usual, Van Rossum was right."
##           <p>
##            <b>
##             Alphabetization:
##            </b>
##            in America, I show up in the alphabet under
## "V".  But in Europe, I show up under "R".  And some of my friends put
## me under "G" in their address book...
##            <h3>
##             More Hyperlinks
##            </h3>
##            <ul>
##             <li>
##              Here's a collection of
##              <a href="http://legacy.python.org/doc/essays/">
##               essays
##              </a>
##              relating to Python
## that I've written, including the foreword I wrote for Mark Lutz' book
## "Programming Python".
##              <p>
##               <li>
##                I own the official
##                <a href="images/license.jpg">
##                 <img align="center" border="0" height="75" src="images/license_thumb.jpg" width="100"/>
##                 Python license.
##                </a>
##                <p>
##                </p>
##               </li>
##              </p>
##             </li>
##            </ul>
##            <h3>
##             The Audio File Formats FAQ
##            </h3>
##            <p>
##             I was the original creator and maintainer of the Audio File Formats
## FAQ.  It is now maintained by Chris Bagwell
## at
##             <a href="http://www.cnpbagwell.com/audio-faq">
##              http://www.cnpbagwell.com/audio-faq
##             </a>
##             .  And here is a link to
##             <a href="http://sox.sourceforge.net/">
##              SOX
##             </a>
##             , to which I contributed
## some early code.
##            </p>
##           </p>
##          </p>
##         </p>
##        </p>
##       </p>
##      </p>
##     </p>
##    </p>
##   </p>
##  </body>
## </html>
## <hr/>
## <a href="images/internetdog.gif">
##  "On the Internet, nobody knows you're
## a dog."
## </a>
## <hr/>
## C:\Users\Dave\AppData\Local\Programs\Python\PYTHON~1\lib\site-packages\bs4\__init__.py:181: UserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system ("html.parser"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.
## 
## The code that caused this warning is on line 119 of the file <string>. To get rid of this warning, change code that looks like this:
## 
##  BeautifulSoup(YOUR_MARKUP})
## 
## to this:
## 
##  BeautifulSoup(YOUR_MARKUP, "html.parser")
## 
##   markup_type=markup_type))
## 
## <title>Guido's Personal Home Page</title>
## 
## 
## Guido's Personal Home Page
## 
## 
## 
## 
## Guido van Rossum - Personal Home Page
## "Gawky and proud of it."
## Who
## I Am
## Read
## my "King's
## Day Speech" for some inspiration.
## 
## I am the author of the Python
## programming language.  See also my resume
## and my publications list, a brief bio, assorted writings, presentations and interviews (all about Python), some
## pictures of me,
## my new blog, and
## my old
## blog on Artima.com.  I am
## @gvanrossum on Twitter.  I
## also have
## a G+
## profile.
## 
## In January 2013 I joined
## Dropbox.  I work on various Dropbox
## products and have 50% for my Python work, no strings attached.
## Previously, I have worked for Google, Elemental Security, Zope
## Corporation, BeOpen.com, CNRI, CWI, and SARA.  (See
## my resume.)  I created Python while at CWI.
## 
## How to Reach Me
## You can send email for me to guido (at) python.org.
## I read everything sent there, but if you ask
## me a question about using Python, it's likely that I won't have time
## to answer it, and will instead refer you to
## help (at) python.org,
## comp.lang.python or
## StackOverflow.  If you need to
## talk to me on the phone or send me something by snail mail, send me an
## email and I'll gladly email you instructions on how to reach me.
## 
## My Name
## My name often poses difficulties for Americans.
## 
## Pronunciation: in Dutch, the "G" in Guido is a hard G,
## pronounced roughly like the "ch" in Scottish "loch".  (Listen to the
## sound clip.)  However, if you're
## American, you may also pronounce it as the Italian "Guido".  I'm not
## too worried about the associations with mob assassins that some people
## have. :-)
## 
## Spelling: my last name is two words, and I'd like to keep it
## that way, the spelling on some of my credit cards notwithstanding.
## Dutch spelling rules dictate that when used in combination with my
## first name, "van" is not capitalized: "Guido van Rossum".  But when my
## last name is used alone to refer to me, it is capitalized, for
## example: "As usual, Van Rossum was right."
## 
## Alphabetization: in America, I show up in the alphabet under
## "V".  But in Europe, I show up under "R".  And some of my friends put
## me under "G" in their address book...
## 
## 
## More Hyperlinks
## 
## Here's a collection of essays relating to Python
## that I've written, including the foreword I wrote for Mark Lutz' book
## "Programming Python".
## I own the official 
## Python license.
## 
## The Audio File Formats FAQ
## I was the original creator and maintainer of the Audio File Formats
## FAQ.  It is now maintained by Chris Bagwell
## at http://www.cnpbagwell.com/audio-faq.  And here is a link to
## SOX, to which I contributed
## some early code.
## 
## 
## 
## "On the Internet, nobody knows you're
## a dog."
## 
## 
## 
## pics.html
## http://www.washingtonpost.com/wp-srv/business/longterm/microsoft/stories/1998/raymond120398.htm
## http://metalab.unc.edu/Dave/Dr-Fun/df200004/df20000406.jpg
## http://neopythonic.blogspot.com/2016/04/kings-day-speech.html
## http://www.python.org
## Resume.html
## Publications.html
## bio.html
## http://legacy.python.org/doc/essays/
## http://legacy.python.org/doc/essays/ppt/
## interviews.html
## pics.html
## http://neopythonic.blogspot.com
## http://www.artima.com/weblogs/index.jsp?blogger=12088
## https://twitter.com/gvanrossum
## https://plus.google.com/u/0/115212051037621986145/posts
## http://www.dropbox.com
## Resume.html
## http://groups.google.com/groups?q=comp.lang.python
## http://stackoverflow.com
## guido.au
## http://legacy.python.org/doc/essays/
## images/license.jpg
## http://www.cnpbagwell.com/audio-faq
## http://sox.sourceforge.net/
## images/internetdog.gif

Acidity of Red Wine:

---

Chapter 2 - Interacting with APIs

Introduction to APIs (Application Programming Interface) and JSON (JavaScript Object Notation):

• API is a protocol and routine for building and interacting with software applications
  
• JSON helps with rel-time browser to server communication, developed by Douglas Crockford
  
• JSON has name-value pairs, very similar to a Python dictionary
  
• General process might include
  • import json
    
  • with open(“snakes.json”, “r”) as json_file: json_data = json.load(json_file) # json_data will be imported as a dictionary

APIs and interacting with the world-wide web - what APIs are and why they are important:

• The API is a set of protocols and routines for interacting with software programs
  
• The “Open Movies Database” (OMDB) has an API, as do most websites that might be data sources
  
• Example usage might include
  • import requests
    
  • url = “http://www.omdbapi.com/?t=hackers” # the ? Represents a “query string”, in this case asking for “t” (title) equals “hackers” (the movie “Hackers”)
    
  • r = requests.get(url)
    
  • json_data = r.json()
    
• Can get the OMDB API webpage for how they allow their data to be queried/used and how to fomat the relevant “query strings”

Example code includes:

myPath = "./PythonInputFiles/"

# DO NOT HAVE FILE a_movie.json, which appears to be JSON for the movie Social Network (2010)
# Created and saved file
import json

# Load JSON: json_data
with open(myPath + "a_movie.json") as json_file:
    json_data = json.load(json_file)

# Print each key-value pair in json_data
for k in json_data.keys():
    print(k + ': ', json_data[k])


# PROBABLY DO NOT RUN; NEED API KEY
# Import requests package
import requests

# Assign URL to variable: url
url = 'http://www.omdbapi.com/?apikey=ff21610b&t=social+network'

# Package the request, send the request and catch the response: r
r = requests.get(url)

# Print the text of the response
print(r.text)

# Decode the JSON data into a dictionary: json_data
json_data = r.json()

# Print each key-value pair in json_data
for k in json_data.keys():
    print(k + ': ', json_data[k])


# Assign URL to variable: url
url = "https://en.wikipedia.org/w/api.php?action=query&prop=extracts&format=json&exintro=&titles=pizza"

# Package the request, send the request and catch the response: r
r = requests.get(url)

# Decode the JSON data into a dictionary: json_data
json_data = r.json()

# Print the Wikipedia page extract
pizza_extract = json_data['query']['pages']['24768']['extract']
print(pizza_extract)

## imdbRating:  7.7
## Rated:  PG-13
## Year:  2010
## DVD:  N/A
## Ratings:  [{'Value': '7.7/10', 'Source': 'Internet Movie Database'}, {'Value': '96%', 'Source': 'Rotten Tomatoes'}, {'Value': '95/100', 'Source': 'Metacritic'}]
## Metascore:  95
## Runtime:  120 min
## Released:  01 Oct 2010
## Plot:  Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, but is later sued by two brothers who claimed he stole their idea, and the co-founder who was later squeezed out of the business.
## Poster:  https://images-na.ssl-images-amazon.com/images/M/MV5BMTM2ODk0NDAwMF5BMl5BanBnXkFtZTcwNTM1MDc2Mw@@._V1_SX300.jpg
## imdbVotes:  508,540
## Director:  David Fincher
## Website:  http://www.thesocialnetwork-movie.com/
## Writer:  Aaron Sorkin (screenplay), Ben Mezrich (book)
## Awards:  Won 3 Oscars. Another 162 wins & 162 nominations.
## Language:  English, French
## Genre:  Biography, Drama
## Type:  movie
## Country:  USA
## Production:  Columbia Pictures
## imdbID:  tt1285016
## Actors:  Jesse Eisenberg, Rooney Mara, Bryan Barter, Dustin Fitzsimons
## BoxOffice:  $96,400,000
## Response:  True
## Title:  The Social Network
## {"Title":"The Social Network","Year":"2010","Rated":"PG-13","Released":"01 Oct 2010","Runtime":"120 min","Genre":"Biography, Drama","Director":"David Fincher","Writer":"Aaron Sorkin (screenplay), Ben Mezrich (book)","Actors":"Jesse Eisenberg, Rooney Mara, Bryan Barter, Dustin Fitzsimons","Plot":"Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, but is later sued by two brothers who claimed he stole their idea, and the co-founder who was later squeezed out of the business.","Language":"English, French","Country":"USA","Awards":"Won 3 Oscars. Another 165 wins & 168 nominations.","Poster":"https://images-na.ssl-images-amazon.com/images/M/MV5BMTM2ODk0NDAwMF5BMl5BanBnXkFtZTcwNTM1MDc2Mw@@._V1_SX300.jpg","Ratings":[{"Source":"Internet Movie Database","Value":"7.7/10"},{"Source":"Rotten Tomatoes","Value":"96%"},{"Source":"Metacritic","Value":"95/100"}],"Metascore":"95","imdbRating":"7.7","imdbVotes":"511,136","imdbID":"tt1285016","Type":"movie","DVD":"N/A","BoxOffice":"$96,400,000","Production":"Columbia Pictures","Website":"http://www.thesocialnetwork-movie.com/","Response":"True"}
## Title:  The Social Network
## Year:  2010
## Rated:  PG-13
## Released:  01 Oct 2010
## Runtime:  120 min
## Genre:  Biography, Drama
## Director:  David Fincher
## Writer:  Aaron Sorkin (screenplay), Ben Mezrich (book)
## Actors:  Jesse Eisenberg, Rooney Mara, Bryan Barter, Dustin Fitzsimons
## Plot:  Harvard student Mark Zuckerberg creates the social networking site that would become known as Facebook, but is later sued by two brothers who claimed he stole their idea, and the co-founder who was later squeezed out of the business.
## Language:  English, French
## Country:  USA
## Awards:  Won 3 Oscars. Another 165 wins & 168 nominations.
## Poster:  https://images-na.ssl-images-amazon.com/images/M/MV5BMTM2ODk0NDAwMF5BMl5BanBnXkFtZTcwNTM1MDc2Mw@@._V1_SX300.jpg
## Ratings:  [{'Source': 'Internet Movie Database', 'Value': '7.7/10'}, {'Source': 'Rotten Tomatoes', 'Value': '96%'}, {'Source': 'Metacritic', 'Value': '95/100'}]
## Metascore:  95
## imdbRating:  7.7
## imdbVotes:  511,136
## imdbID:  tt1285016
## Type:  movie
## DVD:  N/A
## BoxOffice:  $96,400,000
## Production:  Columbia Pictures
## Website:  http://www.thesocialnetwork-movie.com/
## Response:  True
## <p><b>Pizza</b> is a yeasted flatbread typically topped with tomato sauce and cheese and baked in an oven. It is commonly topped with a selection of meats, vegetables and condiments. The term was first recorded in the 10th century, in a Latin manuscript from Gaeta in Central Italy. The modern pizza was invented in Naples, Italy, and the dish and its variants have since become popular and common in many areas of the world.</p>
## <p>In 2009, upon Italy's request, Neapolitan pizza was safeguarded in the European Union as a Traditional Speciality Guaranteed dish. The <i>Associazione Verace Pizza Napoletana</i> (the True Neapolitan Pizza Association) is a non-profit organization founded in 1984 with headquarters in Naples. It promotes and protects the "true Neapolitan pizza".</p>
## <p>Pizza is sold fresh or frozen, either whole or in portions, and is a common fast food item in Europe and North America. Various types of ovens are used to cook them and many varieties exist. Several similar dishes are prepared from ingredients commonly used in pizza preparation, such as calzone and stromboli.</p>

---

Chapter 3 - Diving deeper in to the Twitter API

Twitter API and Authentication - 1) Twitter API, 2) filtering tweets, 3) API Authentication and Oauth, 4) Python package “tweepy”:

• The Twitter API requires a Twitter account, then creating a new Twitter App, then copying over the Token and Token Secret
  
• Twitter has many API including the REST API (Representational State API), which allows for reading and writing Twitter data
  
• The Twitter Streaming API includes a “Public streams” for low-latency access to tweets
  
• The Twitter Firehose API is not publicly avaiable, requires special permission, and would likely be very expensive
  
• Tweets are generally returned as JSON
  
• The “tweepy” package has a nice balance between functionality and usability
  • auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
    
  • auth.set_access(access_token, access_token_secret)

Example code includes:

# DO NOT RUN THIS - NO IDEA WHOSE KEYS THESE ARE (DataCamp???)
# Import package
import tweepy

# Store OAuth authentication credentials in relevant variables
access_token = "1092294848-aHN7DcRP9B4VMTQIhwqOYiB14YkW92fFO8k8EPy"
access_token_secret = "X4dHmhPfaksHcQ7SCbmZa2oYBBVSD2g8uIHXsp5CTaksx"
consumer_key = "nZ6EA0FxZ293SxGNg8g8aP0HM"
consumer_secret = "fJGEodwe3KiKUnsYJC3VRndj7jevVvXbK2D5EiJ2nehafRgA6i"

# Pass OAuth details to tweepy's OAuth handler
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)


# The class MyStreamListener is available at https://gist.github.com/hugobowne/18f1c0c0709ed1a52dc5bcd462ac69f4
# Initialize Stream listener
l = MyStreamListener()

# Create you Stream object with authentication
stream = tweepy.Stream(auth, l)

# Filter Twitter Streams to capture data by the keywords:
stream.filter(track=['clinton', 'trump', 'sanders', 'cruz'])


# Import package
import json

# String of path to file: tweets_data_path
tweets_data_path = "tweets.txt"

# Initialize empty list to store tweets: tweets_data
tweets_data = []

# Open connection to file
tweets_file = open(tweets_data_path, "r")

# Read in tweets and store in list: tweets_data
for line in tweets_file:
    tweet = json.loads(line)
    tweets_data.append(tweet)

# Close connection to file
tweets_file.close()

# Print the keys of the first tweet dict
print(tweets_data[0].keys())


# Import package
import pandas as pd

# Build DataFrame of tweet texts and languages
df = pd.DataFrame(tweets_data, columns=["text", "lang"])

# Print head of DataFrame
print(df.head())


def word_in_text(word, tweet):
    word = word.lower()
    text = tweet.lower()
    match = re.search(word, tweet)

    if match:
        return True
    return False


# Initialize list to store tweet counts
[clinton, trump, sanders, cruz] = [0, 0, 0, 0]

# Iterate through df, counting the number of tweets in which
# each candidate is mentioned
for index, row in df.iterrows():
    clinton += word_in_text('clinton', row['text'])
    trump += word_in_text('trump', row['text'])
    sanders += word_in_text('sanders', row['text'])
    cruz += word_in_text('cruz', row['text'])


# Import packages
import matplotlib.pyplot as plt
import seaborn as sns

# Set seaborn style
sns.set(color_codes=True)

# Create a list of labels:cd
cd = ['clinton', 'trump', 'sanders', 'cruz']

# Plot histogram
ax = sns.barplot(cd, [clinton, trump, sanders, cruz])
ax.set(ylabel="count")
plt.show()

Cleaning Data in Python

Chapter 1 - Exploring Your Data

Diagnose data for cleaning - column names, missing data, outliers, duplicate rows, un-tidy data, unexpected data values, etc.:

• Pandas can be identified/filtered using row/column names or row/column indices
  
• Missing data are typically NaN in Python
  
• For a pandas DataFrame df, df.head() and df.tail() will show the first/last 5 rows
  • df.columns returns an index of column names, which can reveal leading/trailing spaces
    
  • df.shape is analogous to dim() in R
    
  • df.info() will give a summary of the frame, as well as the associated columns (data types, non-missing values, and the like) - note that type “object” means it is non-numeric

Exploratory data analysis - suppose that a pandas DataFrame, df, has already been created:

• To get frequency counts, use df.continent.value_counts(dropna=False) # in this case “continent” is the column name (can also subset using bracket notation, which is required if name has any “problems”). . .
  • Frequency counts will be in descending order
    
  • df[“continent”].value_counts(dropna=False) will return the same thing
    
• To get summaries of numeric data, use df.describe() # will only be run for numeric columns

Visual exploratory data analysis - easy way to spot outliers and obvious errors - assume again that a pandas DataFrame, df, has already been explained:

• Bar plots for discrete data
  
• Histograms for contiuous data - df[“myColumn”].plot(“hist”) will create the histogram, and plt.show() will then show the histogram
  
• Data can be subset similar to R - df[df[“myVar”] condition] will pull only the rows where the specified condition is met
  
• Box plots can be handy ways to summarize the numerical data - df.boxplot(column=“myColumn”, by=“myByVariable”)
  
• Scatter plots can be handy ways to look at relationships between two numeric columns

Example code includes:

# Downloaded small portion to myPath from https://data.cityofnewyork.us/Housing-Development/DOB-Job-Application-Filings/ic3t-wcy2/data

# tempData = pd.read_csv(myPath + "DOB_JOB_Application_Filings.csv")

# keyCols = ["Borough", "State", "Site Fill", "Existing Zoning Sqft", "Initial Cost", "Total Est. Fee"]
# useData = tempData[keyCols]
# useData.loc[:, "initial_cost"] = [float(d[1:]) for d in useData["Initial Cost"]]
# useData.loc[:, "total_est_fee"] = [float(d[1:]) for d in useData["Total Est. Fee"]]
# useData.to_csv(myPath + "dob_job_application_filings_subset.csv")

# MAY NEED TO GET DATA FROM https://opendata.cityofnewyork.us/
# Import pandas
import pandas as pd

myPath = "./PythonInputFiles/"


# Read the file into a DataFrame: df
df = pd.read_csv(myPath + 'dob_job_application_filings_subset.csv')

# Print the head of df
print(df.head())

# Print the tail of df
print(df.tail())

# Print the shape of df
print(df.shape)

# Print the columns of df
print(df.columns)

# Print the info of df
print(df.info())


# Print the value counts for 'Borough'
print(df['Borough'].value_counts(dropna=False))

# Print the value_counts for 'State'
print(df['State'].value_counts(dropna=False))

# Print the value counts for 'Site Fill'
print(df['Site Fill'].value_counts(dropna=False))


# Import matplotlib.pyplot
import matplotlib.pyplot as plt

# Plot the histogram
df['Existing Zoning Sqft'].plot(kind='hist', rot=70, logx=True, logy=True)

# Display the histogram
# plt.show()
plt.savefig("_dummyPy045.png", bbox_inches="tight")
plt.clf()

# Import necessary modules
import pandas as pd
import matplotlib.pyplot as plt

# Create the boxplot
df.boxplot(column="initial_cost", by="Borough", rot=90)

# Display the plot
# plt.show()
plt.savefig("_dummyPy046.png", bbox_inches="tight")
plt.clf()

# Import necessary modules
import pandas as pd
import matplotlib.pyplot as plt

# Create and display the first scatter plot
df.plot(kind="scatter", x="initial_cost", y="total_est_fee", rot=70)
# plt.show()
plt.savefig("_dummyPy047.png", bbox_inches="tight")
plt.clf()

##    Unnamed: 0    Borough State            Site Fill  Existing Zoning Sqft  \
## 0           0   BROOKLYN    NY  USE UNDER 300 CU.YD                     0   
## 1           1   BROOKLYN    NY                  NaN                     0   
## 2           2  MANHATTAN    NY       NOT APPLICABLE                     0   
## 3           3     QUEENS    NY       NOT APPLICABLE                     0   
## 4           4   BROOKLYN    NY       NOT APPLICABLE                     0   
## 
##   Initial Cost Total Est. Fee  initial_cost  total_est_fee  
## 0        $0.00        $420.00           0.0          420.0  
## 1        $0.00        $170.00           0.0          170.0  
## 2    $60000.00        $831.50       60000.0          831.5  
## 3    $31000.00        $692.80       31000.0          692.8  
## 4     $3000.00        $225.00        3000.0          225.0  
##      Unnamed: 0   Borough State            Site Fill  Existing Zoning Sqft  \
## 138         138    QUEENS    NY                  NaN                     0   
## 139         139    QUEENS    NY       NOT APPLICABLE                     0   
## 140         140  BROOKLYN    NY       NOT APPLICABLE                     0   
## 141         141  BROOKLYN    NY  USE UNDER 300 CU.YD                     0   
## 142         142     BRONX    NY                  NaN                     0   
## 
##     Initial Cost Total Est. Fee  initial_cost  total_est_fee  
## 138    $63000.00        $832.40       63000.0          832.4  
## 139    $21000.00        $212.40       21000.0          212.4  
## 140     $2800.00        $395.00        2800.0          395.0  
## 141        $0.00        $472.00           0.0          472.0  
## 142        $0.00        $170.00           0.0          170.0  
## (143, 9)
## Index(['Unnamed: 0', 'Borough', 'State', 'Site Fill', 'Existing Zoning Sqft',
##        'Initial Cost', 'Total Est. Fee', 'initial_cost', 'total_est_fee'],
##       dtype='object')
## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 143 entries, 0 to 142
## Data columns (total 9 columns):
## Unnamed: 0              143 non-null int64
## Borough                 143 non-null object
## State                   143 non-null object
## Site Fill               120 non-null object
## Existing Zoning Sqft    143 non-null int64
## Initial Cost            143 non-null object
## Total Est. Fee          143 non-null object
## initial_cost            143 non-null float64
## total_est_fee           143 non-null float64
## dtypes: float64(2), int64(2), object(5)
## memory usage: 7.3+ KB
## None
## MANHATTAN        66
## BROOKLYN         44
## QUEENS           16
## STATEN ISLAND    10
## BRONX             7
## Name: Borough, dtype: int64
## NY    136
## NJ      6
## NC      1
## Name: State, dtype: int64
## NOT APPLICABLE         108
## NaN                     23
## USE UNDER 300 CU.YD      8
## ON-SITE                  4
## Name: Site Fill, dtype: int64

NYC Open Data Sub-sample (Building Permits - Existing Zoning Sq Ft):

NYC Open Data Sub-sample (Building Permits - Initial Cost by Borough):

NYC Open Data Sub-sample (Building Permits):

---

Chapter 2 - Tidying data for analysis

Tidy data per the Hadley Wickham paper - “standard way to organize data within a dataset”:

• Columns should represent separate variables - if the values are in the column names, then the data need to be melted
  • pd.melt(frame=myFrame, id_vars=[“myID”], value_vars=[“myValues”])
    
  • id_vars will be held fixed; these are the columns that will not be changed during the melting process
    
  • If the value_vars columns are not specified, Python will assume you want to melt all columns other than the ID variables
    
  • The default outputs new columns “variable” and “value”, though these can be over-ridden using var_name=“myVarName” and value_name=“myValName” inside melt
    
• Rows should represent individual observations
  
• Observational units should form tables
  
• There are some trade-offs in reporting vs. data analysis, and tidying the data is primarily for making it easier to analyze

Pivoting data is the opposite of melting; turn unique values in to separate columns (assuming again that the DataFrame, df, already exists):

• To pivot the data, use df.pivot(index=“myIndex”, columns=“myColumns”, values=“myValues”)
  • index is the columns to be fixed
    
  • columns is what is to be pivoted in the new columns
    
  • values is what is to be placed in to the new columns
    
• If there is a duplicate value, the “pivot table” is required, specifying to Python how the duplicated value should be managed
  • df.pivot_table(index=“myIndex”, columns=“myColumns”, values=“myValues”, aggfunc=myFunc) # same as .pivot, but specifying something like np.mean for how to handle duplicates

Beyond melt and pivot - example from the Wickham data of having a single variable that combines sex and age-group (TB data) - common shape for reporting, but less than ideal for analysis:

• First, melt the data so that all these columns become a single “variable” that contains the associated “value”
  
• Second, create new variables for sex and age from the current variable named “variable”
  • tb_melt[“sex”] = tb_melt[“variable”].str[0] # will extract the first character, which is the sex in this case
    
  • tb_melt[“age”] = tb_melt[“variable”].str[1:] # will extract all but the first character

Example code includes:

# THIS SEEMS TO BE THE STANARD R datasets file as a pandas
# Saved airquality.csv to the ./PythonInputFiles

myPath = "./PythonInputFiles/"



import pandas as pd
import numpy as np

airquality = pd.read_csv(myPath + "airquality.csv")


# Print the head of airquality
print(airquality.head())

# Melt airquality: airquality_melt
airquality_melt = pd.melt(airquality, id_vars=["Month", "Day"])

# Print the head of airquality_melt
print(airquality_melt.head())


# Print the head of airquality
print(airquality.head())

# Melt airquality: airquality_melt
airquality_melt = pd.melt(airquality, id_vars=["Month", "Day"], var_name="measurement", value_name="reading")

# Print the head of airquality_melt
print(airquality_melt.head())


# Print the head of airquality_melt
print(airquality_melt.head())

# airquality_melt.pivot() would bomb out on this; not sure why . . . (may be due to having 2+ variables in the index
# Pivot airquality_melt: airquality_pivot
airquality_pivot = airquality_melt.pivot_table(index=["Month", "Day"], columns="measurement", values="reading")

# Print the head of airquality_pivot
print(airquality_pivot.head())


# Print the index of airquality_pivot
print(airquality_pivot.index)

# Reset the index of airquality_pivot: airquality_pivot
airquality_pivot = airquality_pivot.reset_index()

# Print the new index of airquality_pivot
print(airquality_pivot.index)

# Print the head of airquality_pivot
print(airquality_pivot.head())


# Pivot airquality_dup: airquality_pivot
# keyRows = [x for x in range(len(airquality.index))] + [2, 4, 6, 8, 10]
# airquality_dup = airquality.iloc[keyRows, :]
airquality_pivot = airquality_melt.pivot_table(index=["Month", "Day"], columns="measurement", values="reading", aggfunc=np.mean)

# Reset the index of airquality_pivot
airquality_pivot = airquality_pivot.reset_index()

# Print the head of airquality_pivot
print(airquality_pivot.head())

# Print the head of airquality
print(airquality.head())


# tb is 201x18 with variables ['country', 'year', 'm014', 'm1524', 'm2534', 'm3544', 'm4554', 'm5564', 'm65', 'mu', 'f014', 'f1524', 'f2534', 'f3544', 'f4554', 'f5564', 'f65', 'fu']
# year is set to be always 2000 with fu and mu always NaN
# Create dummy data for tb (just use 3 countries and the 014 and 1524 columns)
tb = pd.DataFrame( { "country":["USA", "CAN", "MEX"] , "year":2000 , "fu":np.nan , "mu":np.nan , "f014":[2, 3, 4] , "m014":[5, 6, 7] , "f1524": [8, 9, 0] , "m1524":[1, 2, 3] } )


# Melt tb: tb_melt
tb_melt = pd.melt(tb, id_vars=["country", "year"])

# Create the 'gender' column
tb_melt['gender'] = tb_melt.variable.str[0]

# Create the 'age_group' column
tb_melt['age_group'] = tb_melt.variable.str[1:]

# Print the head of tb_melt
print(tb_melt.head())  # Is now 3,216 x 6 ['country', 'year', 'variable', 'value', 'gender', 'age_group']


# Ebola dataset is available at https://data.humdata.org/dataset/ebola-cases-2014
# Variables are split by an underscore 'Date', 'Day', 'Cases_Guinea', 'Cases_Liberia', 'Cases_SierraLeone', 'Cases_Nigeria', 'Cases_Senegal', 'Cases_UnitedStates', 'Cases_Spain', 'Cases_Mali', 'Deaths_Guinea', 'Deaths_Liberia', 'Deaths_SierraLeone', 'Deaths_Nigeria', 'Deaths_Senegal', 'Deaths_UnitedStates', 'Deaths_Spain', 'Deaths_Mali'

# Downloaded file, then manipulated to be like the above as follows:
# ebola_test = pd.read_csv(myPath + "ebola_data_db_format.csv")
# ebola_test["UseCountry"] = ebola_test["Country"].str.replace(" ", "")
# ebola_test["UseCountry"] = ebola_test["UseCountry"].str.replace("2", "")
# keyIndic = ["Cumulative number of confirmed Ebola deaths", "Cumulative number of confirmed Ebola cases"]
# keyBool = [x in keyIndic for x in ebola_test["Indicator"]]
# ebola_test = ebola_test.loc[keyBool, :]
# indicMap = {keyIndic[0]:"Deaths", keyIndic[1]:"Cases"}
# ebola_test["UseIndicator"] = ebola_test["Indicator"].map(indicMap)
# ebolaPre = ebola_test[["Date", "UseCountry", "UseIndicator", "value"]]
# ebolaPre["CI"] = ebolaPre["UseIndicator"] + "_" + ebolaPre["UseCountry"]
# ebolaSave = ebolaPre.pivot_table(index="Date", columns="CI", values="value", aggfunc="max").fillna(method="ffill").fillna(0)
# ebolaSave.to_csv(myPath + "ebola.csv")

ebola = pd.read_csv(myPath + "ebola.csv", parse_dates=["Date"])


# Melt ebola: ebola_melt
ebola_melt = pd.melt(ebola, id_vars=["Date"], var_name="type_country", value_name="counts")

# Create the 'str_split' column
ebola_melt['str_split'] = ebola_melt["type_country"].str.split("_")

# Create the 'type' column
ebola_melt['type'] = ebola_melt['str_split'].str.get(0)

# Create the 'country' column
ebola_melt['country'] = ebola_melt['str_split'].str.get(1)

# Print the head of ebola_melt
print(ebola_melt.head())


# ebola_melt.to_csv(myPath + "ebola_melt.csv", index=False)
# Run outside of this shell so that the file is accessible later

##    Ozone  Solar.R  Wind  Temp  Month  Day
## 0   41.0    190.0   7.4    67      5    1
## 1   36.0    118.0   8.0    72      5    2
## 2   12.0    149.0  12.6    74      5    3
## 3   18.0    313.0  11.5    62      5    4
## 4    NaN      NaN  14.3    56      5    5
##    Month  Day variable  value
## 0      5    1    Ozone   41.0
## 1      5    2    Ozone   36.0
## 2      5    3    Ozone   12.0
## 3      5    4    Ozone   18.0
## 4      5    5    Ozone    NaN
##    Ozone  Solar.R  Wind  Temp  Month  Day
## 0   41.0    190.0   7.4    67      5    1
## 1   36.0    118.0   8.0    72      5    2
## 2   12.0    149.0  12.6    74      5    3
## 3   18.0    313.0  11.5    62      5    4
## 4    NaN      NaN  14.3    56      5    5
##    Month  Day measurement  reading
## 0      5    1       Ozone     41.0
## 1      5    2       Ozone     36.0
## 2      5    3       Ozone     12.0
## 3      5    4       Ozone     18.0
## 4      5    5       Ozone      NaN
##    Month  Day measurement  reading
## 0      5    1       Ozone     41.0
## 1      5    2       Ozone     36.0
## 2      5    3       Ozone     12.0
## 3      5    4       Ozone     18.0
## 4      5    5       Ozone      NaN
## measurement  Ozone  Solar.R  Temp  Wind
## Month Day                              
## 5     1       41.0    190.0  67.0   7.4
##       2       36.0    118.0  72.0   8.0
##       3       12.0    149.0  74.0  12.6
##       4       18.0    313.0  62.0  11.5
##       5        NaN      NaN  56.0  14.3
## MultiIndex(levels=[[5, 6, 7, 8, 9], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31]],
##            labels=[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]],
##            names=['Month', 'Day'])
## RangeIndex(start=0, stop=153, step=1)
## measurement  Month  Day  Ozone  Solar.R  Temp  Wind
## 0                5    1   41.0    190.0  67.0   7.4
## 1                5    2   36.0    118.0  72.0   8.0
## 2                5    3   12.0    149.0  74.0  12.6
## 3                5    4   18.0    313.0  62.0  11.5
## 4                5    5    NaN      NaN  56.0  14.3
## measurement  Month  Day  Ozone  Solar.R  Temp  Wind
## 0                5    1   41.0    190.0  67.0   7.4
## 1                5    2   36.0    118.0  72.0   8.0
## 2                5    3   12.0    149.0  74.0  12.6
## 3                5    4   18.0    313.0  62.0  11.5
## 4                5    5    NaN      NaN  56.0  14.3
##    Ozone  Solar.R  Wind  Temp  Month  Day
## 0   41.0    190.0   7.4    67      5    1
## 1   36.0    118.0   8.0    72      5    2
## 2   12.0    149.0  12.6    74      5    3
## 3   18.0    313.0  11.5    62      5    4
## 4    NaN      NaN  14.3    56      5    5
##   country  year variable  value gender age_group
## 0     USA  2000     f014    2.0      f       014
## 1     CAN  2000     f014    3.0      f       014
## 2     MEX  2000     f014    4.0      f       014
## 3     USA  2000    f1524    8.0      f      1524
## 4     CAN  2000    f1524    9.0      f      1524
##         Date  type_country  counts        str_split   type country
## 0 2014-08-29  Cases_Guinea   482.0  [Cases, Guinea]  Cases  Guinea
## 1 2014-09-05  Cases_Guinea   604.0  [Cases, Guinea]  Cases  Guinea
## 2 2014-09-08  Cases_Guinea   664.0  [Cases, Guinea]  Cases  Guinea
## 3 2014-09-12  Cases_Guinea   678.0  [Cases, Guinea]  Cases  Guinea
## 4 2014-09-16  Cases_Guinea   743.0  [Cases, Guinea]  Cases  Guinea

---

Chapter 3 - Combining data for analysis

Concatenating data - data may be in separate files (too many records, time series data by day, etc.), while you want to combine it:

• Concatenating data (similar to rbind in R) in Python leaves the original row indices untouched, which can induce duplicate indices
  
• The pd.concat([myFileList]) will place the frames together in a single frame # requires that import pandas as pd was called previously
  • Using ignore_index=True inside pd.concat() will re-cast the row indices from 0 to n-1
    
  • To instead concatenate columns (similar to cbind in R), declare the axis=1 option inside pd.concat()

Finding and concatenating data - issue of many files needing to be concatenated:

• The glob function from the glob library helps to find files based on a consistent search pattern
  • The wildcards * and ? Are both available, with * being any number while ? Is a single character
    
  • Basic usage would be glob.glob(“mySearchString”)
    
• The basic plan would be to 1) load all the files to pandas, and then 2) make a list of the DataFrame names for concatenation

Merge data - extension on concatenation (which is more piecing something back together that was originally one piece but became split):

• Merging can manage joins of tables that never were one piece, combining disparate data based on common columns
  
• The merge syntax is pd.merge(left=leftFrame, right=rightFrame, how=, on=, left_on=, right_on=) # default is an INNER JOIN and need to specify either on= (common variables) or left_on=/right_on=
  • The defaults for on, left_on, and right_on are all None
    
  • The default is for how=“inner” though options for “left”, “right”, and “outer” can also be declared

Example code includes:

myPath = "./PythonInputFiles/"

import pandas as pd
import numpy as np


# uber datasets are a small subset from within http://data.beta.nyc/dataset/uber-trip-data-foiled-apr-sep-2014
# downloaded file "Uber-Jan-Feb-FOIL.csv" to myPath


uber = pd.read_csv(myPath + "Uber-Jan-Feb-FOIL.csv")

cuts = [round(len(uber.index) / 3), round(2 * len(uber.index) / 3)]

uber1 = uber.iloc[:cuts[0], :]
uber2 = uber.iloc[cuts[0]:cuts[1], :]
uber3 = uber.iloc[cuts[1]:, :]

# Save outside of this routine
# uber1.to_csv(myPath + "uber1.csv", index=False)
# uber2.to_csv(myPath + "uber2.csv", index=False)
# uber3.to_csv(myPath + "uber3.csv", index=False)


# Concatenate uber1, uber2, and uber3: row_concat
row_concat = pd.concat([uber1, uber2, uber3])

# Print the shape of row_concat
print(row_concat.shape)

# Print the head of row_concat
print(row_concat.head())

print(np.sum(row_concat != uber))


# ebola_melt is 1,952x4 of Date-Day-status_country-counts
# status_country is 1,952x2 of status-country (the previous status_country has been string split)
# Create this from the file in the previous exercise
ebola_melt = pd.read_csv(myPath + "ebola_melt.csv", parse_dates=["Date"])
ebola_melt.columns = ["Date", "status_country", "counts", "str_split", "status", "country"]

status_country = ebola_melt[["status", "country"]]
ebola_melt = ebola_melt[["Date", "status_country", "counts"]]

# Concatenate ebola_melt and status_country column-wise: ebola_tidy
ebola_tidy = pd.concat([ebola_melt, status_country], axis=1)

# Print the shape of ebola_tidy
print(ebola_tidy.shape)

# Print the head of ebola_tidy
print(ebola_tidy.head())


# Has files ['uber-raw-data-2014_06.csv', 'uber-raw-data-2014_04.csv', 'uber-raw-data-2014_05.csv'] available
# Date/Time-Lat-Lon-Base
# Import necessary modules
import glob
import pandas as pd

# Write the pattern: pattern
# This is designed to get the uber1.csv, uber2.csv, and uber3.csv files
pattern = myPath + 'uber?.csv'

# Save all file matches: csv_files
csv_files = glob.glob(pattern)

# Print the file names
print(csv_files)

# Load the second file into a DataFrame: csv2
csv2 = pd.read_csv(csv_files[1])

# Print the head of csv2
print(csv2.head())


# Create an empty list: frames
frames = []

#  Iterate over csv_files
for csv in csv_files:
    
    #  Read csv into a DataFrame: df
    df = pd.read_csv(csv)
    
    # Append df to frames
    frames.append(df)

# Concatenate frames into a single DataFrame: uber
uber = pd.concat(frames)

# Print the shape of uber
print(uber.shape)

# Print the head of uber
print(uber.head())


# site is a 3x3 with name-lat-long - name=["DR-1", "DR-3", "MSK-4"], lat=[-50, -47, -48.9], lon=[-129, -127, -123.4]
# visited is a 3x3 with ident-site-dated - ident=[619, 734, 837], site=["DR-1", "DR-3", "MSK-4"], dated=["1927-02", "1939-01", "1932-01"]

site = pd.DataFrame( { "name":["DR-1", "DR-3", "MSK-4"], "lat":[-50, -47, -48.9], "lon":[-129, -127, -123.4] } )
visited = pd.DataFrame( { "ident":[619, 734, 837], "site":["DR-1", "DR-3", "MSK-4"], "dated":["1927-02", "1939-01", "1932-01"] } )

# Merge the DataFrames: o2o
o2o = pd.merge(left=site, right=visited, left_on=["name"], right_on=["site"])

# Print o2o
print(o2o)


# now make visited 8x3 with ident=[619, 622, 734, 735, , 751, 752, 837, 844], site=['DR-1', 'DR-1', 'DR-3', 'DR-3', 'DR-3', 'DR-3', 'MSK-4', 'DR-1'], dated=['1927-02-08', '1927-02-10', '1939-01-07', '1930-01-12', '1930-02-26', nan, '1932-01-14', '1932-03-22']

visited = pd.DataFrame( {"ident":[619, 622, 734, 735, 751, 752, 837, 844], "site":['DR-1', 'DR-1', 'DR-3', 'DR-3', 'DR-3', 'DR-3', 'MSK-4', 'DR-1'], "dated":['1927-02-08', '1927-02-10', '1939-01-07', '1930-01-12', '1930-02-26', np.nan, '1932-01-14', '1932-03-22']} )

# Merge the DataFrames: m2o
m2o = pd.merge(left=site, right=visited, left_on=["name"], right_on=["site"])

# Print m2o
print(m2o)


# add an additional frame surveyed which is 21x4 with taken-person-quant-reading (taken matched ident in file visited)
# Merge site and visited: m2m
# m2m = pd.merge(left=site, right=visited, left_on=["name"], right_on=["site"])

# Merge m2m and survey: m2m
# m2m = pd.merge(left=m2m, right=survey, left_on=["ident"], right_on=["taken"])

# Print the first 20 lines of m2m
# print(m2m.head(20))

## (354, 4)
##   dispatching_base_number      date  active_vehicles  trips
## 0                  B02512  1/1/2015              190   1132
## 1                  B02765  1/1/2015              225   1765
## 2                  B02764  1/1/2015             3427  29421
## 3                  B02682  1/1/2015              945   7679
## 4                  B02617  1/1/2015             1228   9537
## dispatching_base_number    0
## date                       0
## active_vehicles            0
## trips                      0
## dtype: int64
## (5180, 5)
##         Date status_country  counts status country
## 0 2014-08-29   Cases_Guinea   482.0  Cases  Guinea
## 1 2014-09-05   Cases_Guinea   604.0  Cases  Guinea
## 2 2014-09-08   Cases_Guinea   664.0  Cases  Guinea
## 3 2014-09-12   Cases_Guinea   678.0  Cases  Guinea
## 4 2014-09-16   Cases_Guinea   743.0  Cases  Guinea
## ['./PythonInputFiles\\uber1.csv', './PythonInputFiles\\uber2.csv', './PythonInputFiles\\uber3.csv']
##   dispatching_base_number       date  active_vehicles  trips
## 0                  B02765  1/20/2015              272   1608
## 1                  B02617  1/20/2015             1350  10015
## 2                  B02764  1/21/2015             3718  27344
## 3                  B02512  1/21/2015              242   1519
## 4                  B02682  1/21/2015             1228   9472
## (354, 4)
##   dispatching_base_number      date  active_vehicles  trips
## 0                  B02512  1/1/2015              190   1132
## 1                  B02765  1/1/2015              225   1765
## 2                  B02764  1/1/2015             3427  29421
## 3                  B02682  1/1/2015              945   7679
## 4                  B02617  1/1/2015             1228   9537
##     lat    lon   name    dated  ident   site
## 0 -50.0 -129.0   DR-1  1927-02    619   DR-1
## 1 -47.0 -127.0   DR-3  1939-01    734   DR-3
## 2 -48.9 -123.4  MSK-4  1932-01    837  MSK-4
##     lat    lon   name       dated  ident   site
## 0 -50.0 -129.0   DR-1  1927-02-08    619   DR-1
## 1 -50.0 -129.0   DR-1  1927-02-10    622   DR-1
## 2 -50.0 -129.0   DR-1  1932-03-22    844   DR-1
## 3 -47.0 -127.0   DR-3  1939-01-07    734   DR-3
## 4 -47.0 -127.0   DR-3  1930-01-12    735   DR-3
## 5 -47.0 -127.0   DR-3  1930-02-26    751   DR-3
## 6 -47.0 -127.0   DR-3         NaN    752   DR-3
## 7 -48.9 -123.4  MSK-4  1932-01-14    837  MSK-4

---

Chapter 4 - Cleaning data for analysis

Data types and conversions - can see the data types using the df.dtypes attribute of a pandas DataFrame df:

• Often helpful to convert strings to numerics or vice versa
  
• The .astype() method will allow for type conversions
  • df[“a”] = df[“a”].astype(str) will create a string variable
    
  • df[“a”] = df[“a”].astype(“category”) will create a categorical (factor) variable
    
  • df[“a”] = pd.to_numeric(df[“a”], errors=“coerce”) will create a numeric variable, with NaN written where the string is not a sensible numeric

Using regular expressions to clean strings - the most common form of data cleaning is string manipulation:

• As an example, monetary values can be represented in many ways
  
• The “re” library is used for pattern matching (using regular expressions) within strings
  • The asterisk (*) means “0 or more times”
    
  • The plus sign (+) means “1 or more times”
    
  • The represents any digit, broadly the same as [0-9], so means zero or more consecutive digits
    
  • The $ means the actual “$” symbol, with the back-slash escaping the symbol from its default meaning as “end-of-string”; so $ will match the dollar sign followed by 0+ digits
    
  • The . Means the actual “.” symbol, with the back-slash escaping the default meaning of the period; so $. will maktc the dollar sign followed by 0+ digits followed by the period followed by 0+ digits
  • The {2} means to have exactly two of the items; sp $. will match “$[0+ digits].[2 digits]”
    
  • The carat means “at the start” and the dollar means “at the end”, so ^$.$ will not match anything with 3+ digits after the period, nor anything with text before the $
    
• Best practices for using an re are to 1) compile the pattern first, then 2) apply the compiled pattern to the pattern
  • pattern = re.compile(“myRegEx”) will compile the specified regular expression for use elsewhere
    
  • result = pattern.match(“myText”) will then pull out the relevant matches to the compiled pattern
    
  • bool(result) will return a True/False as to whether we made any matches

Using functions to clean data - in particular, the .apply() function:

• df.apply(myFunction, myAxis=) # axis 0 is columns, axis 1 is rows, etc.
  
• Example using a few columns with dollar data - check that valid numbers, remove the dollar sign, cast as numeric (NaN if invalid data), store as new column
  • from numpy import NaN
    
  • myVar.replace(“\(", "") will replace the "\)” with “” (more or less, remove the leading dollar signs
    
• Frequently, a function will be passed an entire row of data, so the cleaning can be done for all required variables in the same function
  • df[“myNewVar”] = df.apply(myFunc, axis=1, pattern=pattern) # will pass the rows as argument 1 and pattern as argument 2 to myFunc, once for each row

Duplicate and missing data - can skew results in undesirable manners:

• The df.drop_duplicates() method will remove any rows that are exact duplicates of each other
  
• The df.info() method is a nice way to see how much missing data there is by variable
  
• The df.dropna() method will drop any rows that have any NaN included in them (keeps only the complete.cases() in R syntax)
  
• The df.fillna() allows for replacing either a user-provided value or a calculated value (such as mean/median for the variable where it exists)
  • Can run through multiple columns at the same time by encoding a list; df[[myList]] = df[[myList]].fillna(0) will make NaN in to zero in every column specified in myList

Testing with asserts - early detection for problems that may plague the analysis later:

• More or less, assert myExpression does nothing if True and errors out if False

Example code includes:

# The tips data is available at https://github.com/mwaskom/seaborn-data/blob/master/tips.csv

myPath = "./PythonInputFiles/"

import pandas as pd
import numpy as np

tips = pd.read_csv(myPath + "tips.csv")


# Convert the sex column to type 'category'
tips.sex = tips["sex"].astype("category")

# Convert the smoker column to type 'category'
tips.smoker = tips["smoker"].astype("category")

# Print the info of tips
print(tips.info())


# Convert 'total_bill' to a numeric dtype
tips['total_bill'] = pd.to_numeric(tips["total_bill"], errors="coerce")

# Convert 'tip' to a numeric dtype
tips['tip'] = pd.to_numeric(tips["tip"], errors="coerce")

# Print the info of tips
print(tips.info())


# Import the regular expression module
import re

# Compile the pattern: prog
prog = re.compile('\d{3}-\d{3}-\d{4}')

# See if the pattern matches
result = prog.match('123-456-7890')
print(bool(result))

# See if the pattern matches
result = prog.match("1123-456-7890")
print(bool(result))


# Import the regular expression module
import re

# Find the numeric values: matches
matches = re.findall('\d+', 'the recipe calls for 10 strawberries and 1 banana')

# Print the matches
print(matches)


# Write the first pattern
pattern1 = bool(re.match(pattern='\d{3}-\d{3}-\d{4}', string='123-456-7890'))
print(pattern1)

# Write the second pattern
pattern2 = bool(re.match(pattern='\$\d*\.\d{2}', string='$123.45'))
print(pattern2)

# Write the third pattern
pattern3 = bool(re.match(pattern='[A-Z]\w*', string='Australia'))
print(pattern3)


import numpy

# Define recode_sex()
def recode_sex(sex_value):
    
    # Return 1 if sex_value is 'Male'
    if sex_value == "Male":
        return 1
    
    # Return 0 if sex_value is 'Female'    
    elif sex_value == "Female":
        return 0
    
    # Return np.nan    
    else:
        return np.nan

# Apply the function to the sex column
tips['sex_recode'] = tips["sex"].apply(recode_sex)


# Create the total_dollar field
tips["total_dollar"] = "$" + tips["total_bill"].astype(str)

# Write the lambda function using replace
tips['total_dollar_replace'] = tips["total_dollar"].apply(lambda x: x.replace('$', ''))

# Write the lambda function using regular expressions
tips['total_dollar_re'] = tips["total_dollar"].apply(lambda x: re.findall('\d+\.\d+', x))

# Print the head of tips
print(tips.head())


# DO NOT HAVE DATASET "tracks"
# Create the new DataFrame: tracks
# tracks = billboard[['year', 'artist', 'track', 'time']]

# Print info of tracks
# print(tracks.info())

# Drop the duplicates: tracks_no_duplicates
# tracks_no_duplicates = tracks.drop_duplicates()

# Print info of tracks
# print(tracks_no_duplicates.info())


# SEEMS TO BE "airquality" as per the R datasets package
# Previously saved as myPath + "airquality.csv"
airquality = pd.read_csv(myPath + "airquality.csv")


# Calculate the mean of the Ozone column: oz_mean
oz_mean = airquality["Ozone"].mean()

# Replace all the missing values in the Ozone column with the mean
airquality['Ozone'] = airquality["Ozone"].fillna(oz_mean)

# Print the info of airquality
print(airquality.info())


# DO NOT HAVE FRAME ebola - 122 x 18 of Date-Day-Cases_[8 countries]-Deaths_[8 countries]
# Use the version saved previously
ebola = pd.read_csv(myPath + "ebola.csv", parse_dates=["Date"])

# Assert that there are no missing values
assert ebola.notnull().all().all()

# Assert that all values are >= 0
assert (ebola >= 0).all().all()

## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 244 entries, 0 to 243
## Data columns (total 7 columns):
## total_bill    244 non-null float64
## tip           244 non-null float64
## sex           244 non-null category
## smoker        244 non-null category
## day           244 non-null object
## time          244 non-null object
## size          244 non-null int64
## dtypes: category(2), float64(2), int64(1), object(2)
## memory usage: 8.2+ KB
## None
## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 244 entries, 0 to 243
## Data columns (total 7 columns):
## total_bill    244 non-null float64
## tip           244 non-null float64
## sex           244 non-null category
## smoker        244 non-null category
## day           244 non-null object
## time          244 non-null object
## size          244 non-null int64
## dtypes: category(2), float64(2), int64(1), object(2)
## memory usage: 8.2+ KB
## None
## True
## False
## ['10', '1']
## True
## True
## True
##    total_bill   tip     sex smoker  day    time  size sex_recode total_dollar  \
## 0       16.99  1.01  Female     No  Sun  Dinner     2          0       $16.99   
## 1       10.34  1.66    Male     No  Sun  Dinner     3          1       $10.34   
## 2       21.01  3.50    Male     No  Sun  Dinner     3          1       $21.01   
## 3       23.68  3.31    Male     No  Sun  Dinner     2          1       $23.68   
## 4       24.59  3.61  Female     No  Sun  Dinner     4          0       $24.59   
## 
##   total_dollar_replace total_dollar_re  
## 0                16.99         [16.99]  
## 1                10.34         [10.34]  
## 2                21.01         [21.01]  
## 3                23.68         [23.68]  
## 4                24.59         [24.59]  
## <class 'pandas.core.frame.DataFrame'>
## RangeIndex: 153 entries, 0 to 152
## Data columns (total 6 columns):
## Ozone      153 non-null float64
## Solar.R    146 non-null float64
## Wind       153 non-null float64
## Temp       153 non-null int64
## Month      153 non-null int64
## Day        153 non-null int64
## dtypes: float64(3), int64(3)
## memory usage: 7.2 KB
## None

---

Chapter 5 - Case Study

Putting it all together - Gapminder data (NPO supporting global sustainable development):

• Dataset will be life expectancy by country and year
  
• Goal is to clean and combine all of the data so there is a single file ready for further data analysis

Initial impressions of the data - depending on the analysis needs, can melt (columns to rows) or pivot (new columns from column data) the data:

• Can check the column types by using df.dftypes
  
• Can change column types using .to_numeric() or .astype()
  
• Can save a CSV using df.to_csv(myFile)

Example code includes:

myPath = "./PythonInputFiles/"

# The DataFrame g1800s is a life expectancy table of 260 x 101 - "Life Expectancy" (country) followed by "1800" through "1899"
# Copied data from https://docs.google.com/spreadsheets/d/1H3nzTwbn8z4lJ5gJ_WfDgCeGEXK3PVGcNjQ_U5og8eo/pub as accessed from http://www.gapminder.org/data/ to myPath + "gapminder_lifeExp_1800_1916.xlsx"

import pandas as pd
gapExcel = pd.read_excel(myPath + "gapminder_lifeExp_1800_1916.xlsx")


# Convert column labels to text
gapExcel.columns = gapExcel.columns.astype(str)
assert gapExcel.columns[0] == "Life expectancy"

# Create booleans for 1800s, 1900s, and 2000s, including "Life expectancy" (country columns) as true in all
col1800s = gapExcel.columns.str.startswith("18")
col1900s = gapExcel.columns.str.startswith("19")
col2000s = gapExcel.columns.str.startswith("20")
col1800s[0] = True
col1900s[0] = True
col2000s[0] = True

# Create g1800s, g1900s, g2000s
g1800s = gapExcel.loc[:, col1800s]
g1900s = gapExcel.loc[:, col1900s]
g2000s = gapExcel.loc[:, col2000s]


# Import matplotlib.pyplot
import matplotlib.pyplot as plt

# Create the scatter plot
g1800s.plot(kind="scatter", x="1800", y="1899")

# Specify axis labels
plt.xlabel('Life Expectancy by Country in 1800')
plt.ylabel('Life Expectancy by Country in 1899')

# Specify axis limits
plt.xlim(20, 55)
plt.ylim(20, 55)

# Display the plot
# plt.show()
plt.savefig("_dummyPy048.png", bbox_inches="tight")
plt.clf()


import pandas as pd
import numpy as np


def check_null_or_valid(row_data):
    """Function that takes a row of data,
    drops all missing values,
    and checks if all remaining values are greater than or equal to 0
    """
    no_na = row_data.dropna()[1:-1]
    numeric = pd.to_numeric(no_na)
    ge0 = numeric >= 0
    return ge0

# Check whether the first column is 'Life expectancy'
assert g1800s.columns[0] == "Life expectancy"

# Check whether the values in the row are valid
assert g1800s.iloc[:, 1:].apply(check_null_or_valid, axis=1).all().all()

# Check that there is only one instance of each country
assert g1800s['Life expectancy'].value_counts()[0] == 1


# Also frames g1900s as 260x101 and g2000s as 260x18
# Concatenate the DataFrames row-wise
gapminder = pd.concat([g1800s, g1900s, g2000s])

# Print the shape of gapminder
print(gapminder.shape)

# Print the head of gapminder
print(gapminder.head())


# Melt gapminder: gapminder_melt
gapminder_melt = pd.melt(gapminder, id_vars="Life expectancy")

# Rename the columns
gapminder_melt.columns = ['country', 'year', 'life_expectancy']

# Print the head of gapminder_melt
print(gapminder_melt.head())


# Exercises used gapminder_melt as gapminder - keep copy before over-writing in case needed later
gapminder_old = gapminder.loc[:, :]
gapminder = gapminder_melt.loc[:, :]


# Convert the year column to numeric
gapminder.year = pd.to_numeric(gapminder.year)

# Test if country is of type object
assert gapminder.country.dtypes == np.object

# Test if year is of type int64
assert gapminder.year.dtypes == np.int64

# Test if life_expectancy is of type float64
assert gapminder.life_expectancy.dtypes == np.float64


# Create the series of countries: countries
countries = gapminder["country"]

# Drop all the duplicates from countries
countries = countries.drop_duplicates()

# Write the regular expression: pattern
pattern = '^[A-Za-z\.\s]*$'

# Create the Boolean vector: mask
mask = countries.str.contains(pattern)

# Invert the mask: mask_inverse
mask_inverse = ~mask  # The ~ is for inversion

# Subset countries using mask_inverse: invalid_countries
invalid_countries = countries.loc[mask_inverse]

# Print invalid_countries
print(invalid_countries)


# Assert that country does not contain any missing values
assert pd.notnull(gapminder.country).all()

# Assert that year does not contain any missing values
assert pd.notnull(gapminder.year).all()

# Print the shape of gapminder (prior to dropping NaN)
print(gapminder.shape)

# Drop the missing values
gapminder = gapminder.dropna()

# Print the shape of gapminder (after dropping NaN)
print(gapminder.shape)


# Add first subplot
plt.subplot(2, 1, 1) 

# Create a histogram of life_expectancy
gapminder["life_expectancy"].plot(kind="hist")

# Group gapminder: gapminder_agg
gapminder_agg = gapminder.groupby(by="year")["life_expectancy"].mean()

# Print the head of gapminder_agg
print(gapminder_agg.head())

# Print the tail of gapminder_agg
print(gapminder_agg.tail())


# Add second subplot
plt.subplot(2, 1, 2)

# Create a line plot of life expectancy per year
gapminder_agg.plot()

# Add title and specify axis labels
plt.title('Life expectancy over the years')
plt.ylabel('Life expectancy')
plt.xlabel('Year')

# Display the plots
plt.tight_layout()
# plt.show()
plt.savefig("_dummyPy049.png", bbox_inches="tight")
plt.clf()

# Save both DataFrames to csv files
gapminder.to_csv(myPath + "gapminder.csv")
gapminder_agg.to_csv(myPath + "gapminder_agg.csv")

## (780, 218)
##     1800   1801   1802   1803   1804   1805   1806   1807   1808   1809  \
## 0    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   
## 1  28.21  28.20  28.19  28.18  28.17  28.16  28.15  28.14  28.13  28.12   
## 2    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN    NaN   
## 3  35.40  35.40  35.40  35.40  35.40  35.40  35.40  35.40  35.40  35.40   
## 4  28.82  28.82  28.82  28.82  28.82  28.82  28.82  28.82  28.82  28.82   
## 
##            ...            2008  2009  2010  2011  2012  2013  2014  2015  \
## 0          ...             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
## 1          ...             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
## 2          ...             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
## 3          ...             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
## 4          ...             NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN   
## 
##    2016        Life expectancy  
## 0   NaN               Abkhazia  
## 1   NaN            Afghanistan  
## 2   NaN  Akrotiri and Dhekelia  
## 3   NaN                Albania  
## 4   NaN                Algeria  
## 
## [5 rows x 218 columns]
##                  country  year  life_expectancy
## 0               Abkhazia  1800              NaN
## 1            Afghanistan  1800            28.21
## 2  Akrotiri and Dhekelia  1800              NaN
## 3                Albania  1800            35.40
## 4                Algeria  1800            28.82
## 49           Congo, Dem. Rep.
## 50                Congo, Rep.
## 53              Cote d'Ivoire
## 73     Falkland Is (Malvinas)
## 93              Guinea-Bissau
## 98           Hong Kong, China
## 118     United Korea (former)
## 131              Macao, China
## 132            Macedonia, FYR
## 145     Micronesia, Fed. Sts.
## 161           Ngorno-Karabakh
## 187            St. Barthélemy
## 193    St.-Pierre-et-Miquelon
## 225               Timor-Leste
## 251     Virgin Islands (U.S.)
## 252      North Yemen (former)
## 253      South Yemen (former)
## 258                     Åland
## Name: country, dtype: object
## (169260, 3)
## (43857, 3)
## year
## 1800    31.486020
## 1801    31.448905
## 1802    31.463483
## 1803    31.377413
## 1804    31.446318
## Name: life_expectancy, dtype: float64
## year
## 2012    71.663077
## 2013    71.916106
## 2014    72.088125
## 2015    72.321010
## 2016    72.556635
## Name: life_expectancy, dtype: float64

Gapminder Life Expectancy by Country (1899 vs 1800):

Gapminder Life Expectancy:

Python Data Manipulation

pandas Foundations

Chapter 1 - Data Ingestion and Inspection

Review of pandas data frames - tabular data structure with labelled rows and columns:

• Rows have an index - tabled list of labels
  
• Can get the columns as a list (technically, pandas index) using myPD.columns
  
• Can get the rows as a list (technically, pandas index) using myPD.index
  
• Can filter using numeric indices using myPD.iloc[row, col] # all row or all col is signalled with : and from start end at a-1 is :a and from a to end is a:
  • The .loc accesser will instead access by way of indices
    
• Can see the first few rows using myPD.head() and can see the last few rows using myPD.tail() # put a number inside () if you do not want the default of 6 [indices 0-5]
  
• Can get similar information to str() when using myPD.info()
  
• Can use broadcasting with the :: operator - for example, myPD.iloc[::3, -1] will access every third row and the last column
  
• The columns of a data frame are called a “series”, has its own .head() method, and inherits its name from the master pandas data frame

Building DataFrames from scratch:

• Can load from flat files or other external data sources, such as pd.read_csv()
  
• Can create from dictionaries (associative arrays) - the keys become the column names while the values (lists) become the column contents
  • pd.DataFrame(myDict) will run the conversion, with row indices starting from 0 and running through n-1 created by default
    
• Can create from zipped tuples of lists - assume that lists a, b, and c have already been created and are of the same length
  • list_labels = [“a”, “b”, “c”] ; list_data = [a, b, c] ; zip_list = list(zip(list_labels, list_data))
    
  • pd.DataFrame(dict(zip_list)) will then create the pandas DataFrame by way of the dictionary
    
• New columns can be created on the fly (boradcasting), such as myPD[“newCol”] = 0 # will put 0 in every row of newCol
  • Broadcasting can also be done with the dictionary method, where a single value in a key-value pair will be broadcast to all rows of the DataFrame

Importing and exporting data - example using ISSN_D_tot.csv, sunspot data:

• Can read in the CSV using pd.read_csv(“myCSV.csv”)
  • Appliyng the option header=None will work better for data where the first row does not contain the column labels
    
  • Can also provide the option names=[myList] to assign myList as the column names
    
  • Can also provide the na_values= option to assign NA; for example, na_values=" -1" if all the space followed by -1 are supposed to mean missing values
    
  • Can also provide a dictionary by column names for the NA strings, such as {“sunspots”:[" -1“]} to indicate that the sunspots data column in the CSV uses " -1" for NA
    
  • Can also provide the option parse_dates([myList]) and the reader will do its best to take data in columns myList and amalgamate them to a date
    
• Can keep only the desired columns of a pandas DataFrame by using df[myCols] where myCols is a list of columns desired to be kept
  
• Can write the DataFrame to a CSV using df.to_csv() # Can make other flat files using sep=“”, for example tab-delimited would be sep=“”
  
• Can write the DataFrame to Excel using df.to_excel()

Plotting with pandas - can plot either the panda Series or the underlying numpy array - plt.plot() followed by plt.show() works on either/both:

• myPD[“myCol”].values will be the numpy array for column myCol
  
• myPD[“myCol”] will be the pandas Series for column myCol
  
• Alternately, the pandas Series has a .plot() method, so myPD[“myCol”].plot() rather than plt.plot(myPD[“myCol”]) can be used
  • Can also apply the .plot() method to the full pandas DataFrame, such as myPD.plot()
    
• Can apply plt.yscale(“log”) to create a log-scale for the y-axis
  
• Some additional options to .plot() include color=, style=, legend= # colors are “r”, “b” and the like while styles are " ." and " .-" and the like
  
• Can save plots as various formats, inferred by the extension of the plt.savefig() call
  • PNG plt.savefig(“myFile.png”)
    
  • JPG plt.savefig(“myFile.jpg”)
    
  • PDF plt.savefig(“myFile.pdf”)

Example code includes:

myPath = "./PythonInputFiles/"

# NEED TO CREATE FRAME df - "Total Population" - [3034970564.0, 3684822701.0, 4436590356.0, 5282715991.0, 6115974486.0, 6924282937.0] indexed by "Year" [1960, 1970, 1980, 1990, 2000, 2010]
# Import numpy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


df = pd.DataFrame( {"Total Population":[3034970564.0, 3684822701.0, 4436590356.0, 5282715991.0, 6115974486.0, 6924282937.0], "Year":[1960, 1970, 1980, 1990, 2000, 2010]} )
df.index = df["Year"]
del df["Year"]
world_population = df.copy()

# Create array of DataFrame values: np_vals
np_vals = df.values

# Create new array of base 10 logarithm values: np_vals_log10
np_vals_log10 = np.log10(np_vals)

# Create array of new DataFrame by passing df to np.log10(): df_log10
df_log10 = np.log10(df)

# Print original and new data containers
print(type(np_vals), type(np_vals_log10))
print(type(df), type(df_log10))


list_keys = ['Country', 'Total']
list_values = [['United States', 'Soviet Union', 'United Kingdom'], [1118, 473, 273]]

# Zip the 2 lists together into one list of (key,value) tuples: zipped
zipped = list(zip(list_keys, list_values))

# Inspect the list using print()
print(zipped)

# Build a dictionary with the zipped list: data
data = dict(zipped)

# Build and inspect a DataFrame from the dictionary: df
df = pd.DataFrame(data)
print(df)


tempDict = {"a":[1980, 1981, 1982] , "b":["Blondie", "Chris Cross", "Joan Jett"] , "c":["Call Me", "Arthurs Theme", "I Love Rock and Roll"], "d":[6, 3, 7]}
df = pd.DataFrame(tempDict)

# Build a list of labels: list_labels
list_labels = ['year', 'artist', 'song', 'chart weeks']

# Assign the list of labels to the columns attribute: df.columns
df.columns = list_labels
print(df)


cities = ['Manheim', 'Preston park', 'Biglerville', 'Indiana', 'Curwensville', 'Crown', 'Harveys lake', 'Mineral springs', 'Cassville', 'Hannastown', 'Saltsburg', 'Tunkhannock', 'Pittsburgh', 'Lemasters', 'Great bend']

# Make a string with the value 'PA': state
state = "PA"

# Construct a dictionary: data
data = {'state':state, 'city':cities}

# Construct a DataFrame from dictionary data: df
df = pd.DataFrame(data)

# Print the DataFrame
print(df)


# "world_population.csv is the same 6x2 population data as per the above
# Read in the file: df1
# df1 = pd.read_csv("world_population.csv")
# Skipped this part

# Create a list of the new column labels: new_labels
# new_labels = ["year", "population"]

# Read in the file, specifying the header and names parameters: df2
# df2 = pd.read_csv('world_population.csv', header=0, names=new_labels)
# Skipped this step

# Print both the DataFrames
# print(df1)
# print(df2)


# DO NOT HAVE the messy data - file_messy is "messy_stock_data.tsv"
# Read the raw file as-is: df1
# df1 = pd.read_csv(file_messy)

# Print the output of df1.head()
# print(df1.head())

# Read in the file with the correct parameters: df2
# df2 = pd.read_csv(file_messy, delimiter="\t", header=3, comment="#")

# Print the output of df2.head()
# print(df2.head())

# Save the cleaned up DataFrame to a CSV file without the index
# df2.to_csv(file_clean, index=False)

# Save the cleaned up DataFrame to an excel file without the index
# df2.to_excel('file_clean.xlsx', index=False)



# DO NOT HAVE DataFrame df, which is a 744x1 of "Temperature (deg F)" indexed automatically as 0-743
# Downloaded raw METAR data for KAUS using 0801100000 UTC - 0831102359 UTC
# Coded to a cleaned CSV as per below
# 
# 
# metarList = []
# for line in open(myPath + "KAUS_Metar_Aug2010.txt", "r"): metarList.append(line.rstrip())
# cleanMetar = []
# cleanLine = ""
# for recs in metarList:
#     if recs.startswith("#") or recs == "" : continue
#     if recs.startswith("2") : 
#         if cleanLine != "" : 
#             cleanMetar.append(cleanLine)
#         cleanLine = recs
#     else:
#         cleanLine = cleanLine + " " + recs.strip()
# 
# cleanMetar.append(cleanLine)
# 
# useMetar = [textBlock for textBlock in cleanMetar if "METAR" in textBlock]
# useSpeci = [textBlock for textBlock in cleanMetar if "SPECI" in textBlock]
# assert len(cleanMetar) == len(useMetar) + len(useSpeci)
# 
# import re
# 
# metTime = []
# tempF = []
# dewF = []
# altMG = []
# 
# for textBlock in useMetar:
#     if textBlock.endswith("NIL="):
#         print("Not using line", textBlock)
#         continue
#     
#     # print(textBlock)
#     dateUTC = textBlock.split()[0]
#     
#     tempData = re.findall("T([0-9][0-9][0-9][0-9])([0-9][0-9][0-9][0-9])", textBlock)
#     assert len(tempData) == 1
#     a, b = tempData[0]
#     tempC = float(a[1:])/10
#     dewC = float(b[1:])/10
#     if a[0] == "1" : tempC = -tempC
#     if b[0] == "1" : dewC = -dewC
#     
#     tF = round((9/5) * tempC + 32, 0)
#     dF = round((9/5) * dewC + 32, 0)
#     
#     altData = re.findall("A([0-9][0-9][0-9][0-9])", textBlock)
#     assert len(altData) == 1
#     
#     aMG = float(altData[0]) / 100
#     # print(dateUTC, tempC, dewC, altMG, tempF, dewF)
#     
#     metTime.append(dateUTC)
#     tempF.append(tF)
#     dewF.append(dF)
#     altMG.append(aMG)
# 
# metarKAUS = pd.DataFrame( {"DateTime (UTC)":metTime, "Temperature (deg F)":tempF , "Dew Point (deg F)":dewF, "Pressure (atm)":altMG} )
# metarKAUS.index = metarKAUS["DateTime (UTC)"]
# del metarKAUS["DateTime (UTC)"]
# 
# metarKAUS.to_csv(myPath + "KAUS_Metar_Aug2010_Clean.csv")


# Create or import the data
# import random
# df = pd.DataFrame( {"Temperature (deg F)":np.random.randint(low=60, high=100, size=744)} )
dfFull = pd.read_csv(myPath + "KAUS_Metar_Aug2010_Clean.csv")
df = dfFull.loc[:, "Temperature (deg F)"]

# Create a plot with color='red'
df.plot(color="red")

# Add a title
plt.title('Temperature in Austin')

# Specify the x-axis label
plt.xlabel('Hours since midnight August 1, 2010')

# Specify the y-axis label
plt.ylabel('Temperature (degrees F)')

# Display the plot
# plt.show()
plt.savefig("_dummyPy050.png", bbox_inches="tight")
plt.clf()


# DO NOT HAVE DataFrame df, which is a 744x3 of "Temperature (deg F)", "Dew Point (deg F)", "Pressure (atm)" indexed automatically as 0-743
# df["Dew Point (deg F)"] = df.iloc[:, 0] + np.random.randint(low=-30, high=0, size=744)
# df["Pressure (atm)"] = np.random.randint(low=980, high=1020, size=744)
# Use dfFull rather than manufacturing data

df = dfFull.copy()
df.index = [x[6:8] + "-" + "{0:0>2}".format(str(int(x[9:10]) + 1)) + "Z" for x in df["DateTime (UTC)"].astype(str)]
del df["DateTime (UTC)"]

# Plot all columns (default)
df.plot()
# plt.show()
plt.savefig("_dummyPy051.png", bbox_inches="tight")
plt.clf()


# Plot all columns as subplots
df.plot(subplots=True)
# plt.show()
plt.savefig("_dummyPy052.png", bbox_inches="tight")
plt.clf()


# Plot just the Dew Point data
column_list1 = ['Dew Point (deg F)']
df[column_list1].plot()
# plt.show()
plt.savefig("_dummyPy053.png", bbox_inches="tight")
plt.clf()


# Plot the Dew Point and Temperature data, but not the Pressure data
column_list2 = ['Temperature (deg F)','Dew Point (deg F)']
df[column_list2].plot()
# plt.show()
plt.savefig("_dummyPy054.png", bbox_inches="tight")
plt.clf()

## <class 'numpy.ndarray'> <class 'numpy.ndarray'>
## <class 'pandas.core.frame.DataFrame'> <class 'pandas.core.frame.DataFrame'>
## [('Country', ['United States', 'Soviet Union', 'United Kingdom']), ('Total', [1118, 473, 273])]
##           Country  Total
## 0   United States   1118
## 1    Soviet Union    473
## 2  United Kingdom    273
##    year       artist                  song  chart weeks
## 0  1980      Blondie               Call Me            6
## 1  1981  Chris Cross         Arthurs Theme            3
## 2  1982    Joan Jett  I Love Rock and Roll            7
##                city state
## 0           Manheim    PA
## 1      Preston park    PA
## 2       Biglerville    PA
## 3           Indiana    PA
## 4      Curwensville    PA
## 5             Crown    PA
## 6      Harveys lake    PA
## 7   Mineral springs    PA
## 8         Cassville    PA
## 9        Hannastown    PA
## 10        Saltsburg    PA
## 11      Tunkhannock    PA
## 12       Pittsburgh    PA
## 13        Lemasters    PA
## 14       Great bend    PA

Temperature - Austin, TX (Aug 2010):

METAR plots - Austin, TX (Aug 2010):

METAR Sub-plots - Austin, TX (Aug 2010):

Dew Point - Austin, TX (Aug 2010):

Temperature and Dew Point - Austin, TX (Aug 2010):

---

Chapter 2 - Exploratory Data Analysis

Visual exploratory data analysis - using Fisher’s iris flower data (similar to the R dataset):

• Can use df.plot(x=“quotedVar1”, y=“quotedVar2”, kind=“scatter”) followed by plt.show() for general DataFrame plotting
  • The default is kind=“line”, though kind=“scatter” often makes more sense for unordered and/or multi-dimensional data
    
  • Can add plt.xlabel() and plt.ylabel() for labelling the axis dimensions
    
  • Can also have types like kind=“box” for box/whiskers, kind=“hist” for histograms, etc.
    
  • Further, can specify any matplotlib options inside DataFrame.plot() command - see the documentation
    
• For histograms, cumulative=True will make the CDF rather than PDF while normed=True makes it probabilities rather than total counts
  
• There are several manners (with slightly different defaults) for calling plots on a dataframe - df.plot(kind=“hist”), df.plt.hist(), and df.hist()

Statistical exploratory data analysis - starting with the .describe() method which is very similar to summary() in R - counts, means, quartiles, and the like:

• These can be accessed individually, such as .count(), .mean(), .std(), .median(), .quantile(q) where q is between 0 and 1 and can be a list or array of values, .max(), .min()
  • All of these statistics AVOID the null entries - the count is the count of non-null, the mean is the mean of the non-null, etc.

Separating populations with boolean indexing - subsets of columns and/or rows for plotting, summarizing, and the like:

• The .unique() method returns the unique factors of a categorical variable, suggesting subsets of interest for EDA
  
• The typical filtering process would be to create a boolean, then myFilter = myDF[myBool, :]

Example code includes:

myPath = "./PythonInputFiles/"


import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt


dummyStock = pd.read_csv(myPath + "StockChart_20170615.csv", header=None)
dummyStock.columns = ["Symbol", "Data"]
# Data is a single space-delimited string of Date - Open - High - Low - Close - Volume

dummyStockSplit = dummyStock["Data"].str.split()
dummyDates = [datetime.strptime(x[0], "%m/%d/%Y") for x in dummyStockSplit]
dummyClose = [float(x[4]) for x in dummyStockSplit]

dfStock = pd.DataFrame( {"date":dummyDates, "symbol":dummyStock["Symbol"] , "close":dummyClose} )
df = dfStock.pivot(index="date", columns="symbol", values="close").resample("M").max()


# df is 12 x 4 with columns Month-AAPL-GOOG-IBM
# Create a list of y-axis column names: y_columns
y_columns = ["AAPL", "IBM"]

# Generate a line plot
df.plot(y=y_columns)

# Add the title
plt.title('Monthly stock prices')

# Add the y-axis label
plt.ylabel('Price ($US)')

# Display the plot
# plt.show()
plt.clf()


# Here, df appears to be the mtcars data
# Saved file from R
df = pd.read_csv(myPath + "mtcars.csv", index_col=0)

# sizes is a pre-defined np.array(), not sure of what
sizes = df["cyl"]
# Generate a scatter plot
df.plot(kind="scatter", x='hp', y='mpg', s=5*(sizes-3))

# Add the title
plt.title('Fuel efficiency vs Horse-power')

# Add the x-axis label
plt.xlabel('Horse-power')

# Add the y-axis label
plt.ylabel('Fuel efficiency (mpg)')

# Display the plot
# plt.show()
plt.clf()


# Make a list of the column names to be plotted: cols
cols = ["wt", "mpg"]

# Generate the box plots
df[cols].plot(kind="box", subplots=True)

# Display the plot
# plt.show()
plt.clf()


# Here, df is the tipping data from the Seaborn package, with emphasis on the column "fraction"
# Create a reasonable analog based on the pre-made CSV
tips = pd.read_csv(myPath + "tips.csv")
tips.sex = tips["sex"].astype("category")
tips.smoker = tips["smoker"].astype("category")
tips['total_bill'] = pd.to_numeric(tips["total_bill"], errors="coerce")
tips['tip'] = pd.to_numeric(tips["tip"], errors="coerce")
tips["fraction"] = tips["tip"] / tips["total_bill"]
df = tips.copy()


# This formats the plots such that they appear on separate rows
fig, axes = plt.subplots(nrows=2, ncols=1)

# Plot the PDF and CDF on the two axes
df.fraction.plot(ax=axes[0], kind='hist', bins=30, normed=True, range=(0,.3))
df.fraction.plot(ax=axes[1], kind="hist", bins=30, normed=True, cumulative=True, range=(0,.3))
# plt.show()
plt.clf()


# df is degrees by gender from http://nces.ed.gov/programs/digest/2013menu_tables.asp
# DO NOT HAVE DATASET - skip
# Print the minimum value of the Engineering column
# print(df["Engineering"].min())

# Print the maximum value of the Engineering column
# print(df["Engineering"].max())

# Construct the mean percentage per year: mean
# mean = df.mean(axis="columns")

# Plot the average percentage per year
# mean.plot()

# Display the plot
# plt.show()


# Now, df appears to be the Titanic dataset (not the table)
df = pd.read_csv(myPath + "titanic.csv")

# Print summary statistics of the fare column with .describe()
print(df["Fare"].describe())

# Generate a box plot of the fare column
df["Fare"].plot(kind="box")

# Show the plot
# plt.show()
plt.clf()


# Now, df is the life-expectancy Gapminder data as 260x219
# Needs the encoding to load
df = pd.read_csv(myPath + "gapminder.csv", encoding="latin-1", index_col=0).pivot_table(index="country", columns="year", values="life_expectancy")

# Print the number of countries reported in 2015
print(df[2015].count())

# Print the 5th and 95th percentiles
print(df.quantile([0.05, 0.95]))

# Generate a box plot
years = [1800, 1850, 1900, 1950, 2000]
df[years].plot(kind='box')
# plt.show()
plt.clf()


# Now, df is Pittsburgh weather data from https://www.wunderground.com/history/
# NEED TO GET THIS DATA
# january and march are both 31x2 with the columns being Date-Temperature
# Print the mean of the January and March data
# print(january.mean(), march.mean())

# Print the standard deviation of the January and March data
# print(january.std(), march.std())


# Here, df is again automobile data of shape (392, 9)
# NEED TO GET THIS DATA
# Compute the global mean and global standard deviation: global_mean, global_std
# global_mean = df.mean()
# global_std = df.std()

# Filter the US population from the origin column: us
# us = df.loc[df["origin"] == "US", :]

# Compute the US mean and US standard deviation: us_mean, us_std
# us_mean = us.mean()
# us_std = us.std()

# Print the differences
# print(us_mean - global_mean)
# print(us_std - global_std)


# titanic is 1309x14 of data from the titanic
titanic = pd.read_csv(myPath + "titanic.csv", index_col=0)


# Display the box plots on 3 separate rows and 1 column
fig, axes = plt.subplots(nrows=3, ncols=1)

# Generate a box plot of the fare prices for the First passenger class
titanic.loc[titanic['Pclass'] == 1].plot(ax=axes[0], y='Fare', kind='box')

# Generate a box plot of the fare prices for the Second passenger class
titanic.loc[titanic['Pclass'] == 2].plot(ax=axes[1], y='Fare', kind='box')

# Generate a box plot of the fare prices for the Third passenger class
titanic.loc[titanic['Pclass'] == 3].plot(ax=axes[2], y='Fare', kind='box')

# Display the plot
# plt.show()
plt.clf()

## count    891.000000
## mean      32.204208
## std       49.693429
## min        0.000000
## 25%        7.910400
## 50%       14.454200
## 75%       31.000000
## max      512.329200
## Name: Fare, dtype: float64
## 208
## year   1800   1801   1802  1803  1804   1805   1806   1807  1808  1809  \
## 0.05  25.40  25.30  25.20  25.2  25.2  25.40  25.40  25.40  25.3  25.3   
## 0.95  37.92  37.35  38.37  38.0  38.3  38.37  38.37  38.37  38.0  38.0   
## 
## year   ...      2007   2008    2009    2010   2011    2012    2013   2014  \
## 0.05   ...     53.07  53.60  54.235  54.935  55.97  56.335  56.705  56.87   
## 0.95   ...     80.73  80.93  81.200  81.365  81.60  81.665  81.830  82.00   
## 
## year    2015     2016  
## 0.05  57.855  59.2555  
## 0.95  82.100  82.1650  
## 
## [2 rows x 217 columns]

---

Chapter 3 - Time series in pandas